diff --git a/final/README.md b/final/README.md
index 2ea0362..5589310 100644
--- a/final/README.md
+++ b/final/README.md
@@ -11,7 +11,7 @@
 | `q1/` | Q1 原生特征提取、物理时间特征包构建、五折对照和可视化 |
 | `q2/math/` | 数学方案 C0–C7、训练、缺失控制评估及附件 3 预测 |
 | `q2/deep_learning/q2/` | EarlyConcat + BiGRU、MoFE-7 + MLP Router 及训练协议 |
-| `q3/` | Q3 训练、验证、附件 4 全量预测和逐样本解释卡片 |
+| `q3/` | Q3 验证、附件 4 全量预测和逐样本解释卡片 |
 | `output/q1/` | 已整理的 Q1 100 个特征文件、对齐审计和已有结果 |
 | `output/q2/` | 已整理的 Q2 未对齐数据模型对比结果表 |
 | `output/q3/` | Q3 运行后生成的预测、解释和验证结果 |
@@ -148,28 +148,47 @@ python final/compare_unaligned_q2.py \
 
 结果文件包括 `comparison_validation.csv`（全模型验证对比）、`comparison_test.csv`（预先选出的数学模型及两种深度模型测试结果）、`comparison_aurc.csv`（四种缺失模式的 AURC-MAE）。指标定义、已有对比数值和边界说明见 [REPORTS.md](REPORTS.md)。
 
-## Q3：可解释预测与附件 4 输出
+## Q3：分层反事实证据归因
 
-Q3 使用附件 2 训练集训练 EarlyConcat + BiGRU；只用官方验证集选择 epoch。默认最多 12 轮、耐心值 3、batch size 64，AdamW 学习率 `3e-4`、权重衰减 `1e-3`，目标函数为交叉熵加 `0.5 × SmoothL1` 强度回归。验证选择使用自然缺失、30% 单模态/同步缺失和 50% 异步缺失情景。附件 4 的 20 个未对齐样本经同一 adapter 投影，生成类别、情感分数、三类概率、模态贡献、主导模态及逐样本解释卡片。
+Q3 复用 Q2 已训练的 EarlyConcat + BiGRU、MoFE-7 + MLP Router 检查点和同一套训练集 robust scaler，不重新拟合模型。第一轮含三个解释方案：E0 对 EarlyConcat 做精确三模态 Shapley 与局部遮蔽；E1 把 MoFE Router 当作待检验的内部信号；E2 对同一个 MoFE 检查点做精确 Shapley、交互和局部遮蔽。E1/E2 的预测完全相同，比较的是解释方式。
 
-```bash
-python -m final.q3.train_interpretable \
-  --input-version unaligned_50 \
-  --attachment4-version unaligned_50 \
-  --output-dir final/output/q3 \
-  --device auto
-```
+在项目根目录设置附件位置后运行。项目默认查找 final/data/；也可以将 FINAL_DATA_DIR 指向包含附件 2、附件 4 文件夹的根目录：
 
-输出目录包含：
+~~~powershell
+$env:FINAL_DATA_DIR = "D:\task-data"
+python -m final.q3.run_experiments --output-dir final/output/q3/first_round --device auto
+~~~
 
-- `attachment4_predictions.csv`：20 条完整预测及类别概率。
-- `attachment4_explanations.csv`：预测、各模态遮蔽影响、主导模态和原始转写。
-- `attachment4_local_evidence.csv`：文本 token、音频/视频来源行、归一化进程区间和单位置遮蔽影响。
-- `attachment4_input_audit.csv`：输入哈希、adapter 模式、长度和可见位置审计。
-- `explanation_cards/`：逐样本 Markdown 解释卡；`typical_explanation_card.md` 为置信度接近样本中位数的代表样例。
-- `validation_metrics.json`、`validation_predictions.csv`、`validation_errors.csv`、`validation_diagnostics.png`：验证指标、误差样本和图示。
+~~~bash
+export FINAL_DATA_DIR="/data/task-data"
+python -m final.q3.run_experiments --output-dir final/output/q3/first_round --device auto
+~~~
 
-附件 4 未对齐特征没有可靠物理时间戳，因此音频/视频证据位置以归一化进程和原始特征行表示，不伪造秒级时间。模态/位置分数由遮蔽特征前后的模型概率差计算，表示模型敏感性，不是因果效应或情感成因证明。输出中提供源视频相对路径，便于回到原片段查看。
+完整运行使用官方验证集生成预测指标、误差归因和分类 margin 的 Shapley。只想快速检查附件 4 可增加 --skip-validation；不抽取候选视频帧可增加 --no-frames。每次运行请给一个新的空输出目录。
+
+主要产物：
+
+- attachment4_predictions.csv：20 个附件 4 样本在 E0/E1/E2 下的预测和类别概率。
+- attachment4_modal_shapley.csv、attachment4_pairwise_interactions.csv：分类 logit 与强度回归的模态贡献、绝对贡献比例、配对交互和 Shapley 完备性残差。
+- attachment4_local_evidence.csv、attachment4_router_local_evidence.csv：1/3/5 个相对进程 bin 的局部遮蔽响应与 MoFE Router 位置分数。
+- attachment4_evidence_segments.csv：每个方案的代表性文本、音频和视觉证据段；evidence_frames/ 中保存由进度比例估算位置抽取的候选帧。
+- faithfulness_by_sample.csv、q3_method_comparison.csv：删除/保留检验、0–70% 删除曲线、尺度稳定性和 Router–Shapley 一致性汇总。
+- validation_predictions.csv、validation_errors.csv、validation_error_attribution.csv：官方验证集预测、误差样本和分类 margin 归因。
+- explanation_cards/、typical_explanation_card.md、evidence_profiles/：逐样本解释卡、代表卡和 3×50 局部证据图。
+
+想先查看文本、音频波形、视频帧中的遮蔽位置，再查看对应时间 bin 上七个 MoFE expert 的 Router 权重热力图，可运行：
+
+~~~powershell
+python -m final.q3.plot_router_heatmaps --output-dir final/output/q3
+~~~
+
+它输出附件 4 样本 02、03 的完整输入和受控残缺对照图：蓝色斜线表示输入被遮蔽，下面按 T、A、V、TA、TV、AV、TAV 顺序显示 expert 的 α[t,e]。详细定义与限制见 [Q3 实验说明](q3/README.md#router-热力图样例)，实验图也已收录在 [REPORTS.md](REPORTS.md)。
+
+三种输入模态只有 8 个 coalition，脚本完整枚举而非近似 SHAP。分类解释固定使用完整输入的预测类别 logit，回归解释使用情感强度输出。局部证据是单模态窗口被遮蔽前后的 logit 差；正值表示该窗口支持当前预测，负值表示该窗口反对当前预测。Router 权重只描述路由机制，不能直接解释成预测贡献。
+
+附件 4 特征行没有物理时间戳。文本可以回溯到 tokenizer token 和来源行；音频/视觉证据保留来源行与归一化进程。若视频可读，候选帧位置由相对进程乘视频时长估算，供人工回看，不表示特征已经物理时间对齐。删除/保留检验使用模型训练时见过的 mask 接口，但孤立稀疏遮蔽仍可能偏离训练分布；报告会把 10% 保留结果标为诊断，主要删除曲线范围限制在最多删除 70%。
+
+细节和解释边界见 Q3 实验说明（q3/README.md）与 REPORTS.md。
 
 ## 模型与对齐约定
 
@@ -182,4 +201,4 @@ python -m final.q3.train_interpretable \
 
 训练结果写入传入的 `--output-dir`；建议为每轮实验使用新的空目录。检查点、训练历史、数据/划分哈希、随机种子、特征版本、adapter 审计和验证/测试指标随运行结果保存。官方附件不进入版本库；`final/.gitignore` 已忽略 `data/`、Q1 中间缓存和环境目录。
 
-当前已整理的 Q1/Q2 表格和结果摘要保存在 `output/`、`experiments/` 与 [REPORTS.md](REPORTS.md)。若要重建所有模型权重和预测，按上面的 Q1、Q2、Q3 顺序运行相应入口；Q1 原生视觉特征还需要单独准备 OpenFace 2.2.0。
+当前已整理的 Q1/Q2/Q3 表格和结果摘要保存在 `output/`、`experiments/` 与 [REPORTS.md](REPORTS.md)。若要重建所有模型权重和预测，按上面的 Q1、Q2、Q3 顺序运行相应入口；Q1 原生视觉特征还需要单独准备 OpenFace 2.2.0。
diff --git a/final/REPORTS.md b/final/REPORTS.md
index 5b177e9..5e73ba9 100644
--- a/final/REPORTS.md
+++ b/final/REPORTS.md
@@ -94,4 +94,47 @@ Q1 adapter 使用带媒体时戳的原生特征进行物理时间聚合；Q2 未
 
 在两种深度学习方案之间，MoFE − EarlyConcat 的 AURC-MAE 配对 95% 区间分别为：单模态 -0.0104 [-0.0347, 0.0111]、同步 -0.0107 [-0.0322, 0.0093]、部分重叠 -0.0075 [-0.0280, 0.0112]、异步 -0.0026 [-0.0219, 0.0149]；均跨零。数学分支 C6 的四种 AURC 点估计均低于 C0，但这是描述性点差；不把它解读为跨随机种子的稳健优势。
 
-完整精度及审计文件：[14 个版本验证指标](output/q2/comparison_validation.csv) · [3 个正式测试结果](output/q2/comparison_test.csv) · [全部版本 AURC-MAE](output/q2/comparison_aurc.csv) · [数学运行清单](experiments/q2/unaligned_math_all_b128/run_manifest.json) · [深度学习运行清单](experiments/q2/unaligned_deep_two_b128/run_manifest.json) · [数学 42 情景明细（gzip CSV）](experiments/q2/unaligned_math_all_b128/controlled_missingness.csv.gz) · [深度学习 42 情景明细](experiments/q2/unaligned_deep_two_b128/controlled_metrics_by_scenario.csv) · [深度学习测试配对 Bootstrap](experiments/q2/unaligned_deep_two_b128/official_test_paired_bootstrap.csv)。
+完整精度及审计文件：[14 个版本验证指标](output/q2/comparison_validation.csv) · [3 个正式测试结果](output/q2/comparison_test.csv) · [全部版本 AURC-MAE](output/q2/comparison_aurc.csv) · [数学运行清单](experiments/q2/unaligned_math_all_b128/run_manifest.json) · [深度学习运行清单](experiments/q2/unaligned_deep_two_b128/run_manifest.json) · [数学 42 情景明细（gzip CSV）](experiments/q2/unaligned_math_all_b128/controlled_missingness.csv.gz) · [深度学习 42 情景明细](experiments/q2/unaligned_deep_two_b128/controlled_metrics_by_scenario.csv) · [深度学习测试配对 Bootstrap](experiments/q2/unaligned_deep_two_b128/official_test_paired_bootstrap.csv)。
+
+## Q3：分层反事实证据归因（第一轮）
+
+Q3 将 Q1 的统一输入/回溯接口、Q2 的缺失鲁棒预测器与附件 4 原始视频接起来。附件 4 的特征按统一 adapter 的 Relative-Progress 模式转换；预测器直接复用 Q2 seed 20260924 的 EarlyConcat + BiGRU、MoFE-7 + MLP Router 检查点和仅由训练集拟合的 robust scaler，本轮不重新训练。解释覆盖附件 4 的 20 个样本；官方验证集另有 728 条样本用于复核预测指标并输出误差归因。
+
+三模态只有 8 个输入组合，因此 E0/E2 对文本、音频、视觉 coalition 完整枚举，分别计算预测类别 logit 与强度输出的精确 Shapley 贡献及成对交互。局部证据由窗口宽度 1/3/5 的反事实遮蔽得到，并回溯到特征来源行、文本片段及候选视频帧。E1 展示 MoFE Router 权重，并用同一预测器做删除/保留检验；Router 权重作为内部路由信号分析，不直接当作预测贡献。
+
+### 预测器验证表现
+
+下表为复用检查点在官方验证集自然缺失输入上的结果。E1 与 E2 使用同一个 MoFE 检查点，因此预测指标相同。
+
+| 方案 / 预测器 | Accuracy ↑ | Macro-F1 ↑ | MAE ↓ | RMSE ↓ | Pearson ↑ |
+|---|---:|---:|---:|---:|---:|
+| E0 EarlyConcat + BiGRU | **0.6236** | **0.5779** | 0.6614 | 0.8731 | **0.6092** |
+| E1/E2 MoFE-7 + MLP Router | 0.6140 | 0.5661 | **0.6426** | **0.8499** | 0.6014 |
+
+EarlyConcat 的 Accuracy/Macro-F1 点估计较高，MoFE 的 MAE/RMSE 较低。这里复核的是已训练检查点，不是 Q3 新模型训练结果；指标差别不代表显著差异。
+
+### 附件 4 解释行为检查
+
+下表数值是 20 个无人工解释标签样本的均值。Comprehensiveness 是删除高排名局部证据后预测类别 logit 的下降，越大说明当前预测对所选证据越敏感；Sufficiency 是只保留高排名证据后的完整输入 logit 绝对差，越小越接近完整输入。Deletion AUC 汇总删除 0%–70% 的 logit 下降。
+
+| 方案 | 删除 Top 10% ↑ | 删除 Top 30% ↑ | 仅保留 Top 10% 的绝对差 ↓ | 仅保留 Top 30% 的绝对差 ↓ | Deletion AUC ↑ | 尺度稳定性 Spearman ↑ |
+|---|---:|---:|---:|---:|---:|---:|
+| E0 EarlyConcat：Shapley + 局部遮蔽 | 0.4599 | **1.1218** | 0.5246 | 0.2542 | **0.9157** | 0.9047 |
+| E1 MoFE：Router + 反事实检验 | 0.2100 | 0.8702 | 0.4954 | 0.2500 | 0.7925 | — |
+| E2 MoFE：Shapley + 局部遮蔽 | **0.3515** | 0.9818 | **0.4116** | **0.1950** | 0.8371 | 0.8823 |
+
+在同一 MoFE 检查点上，E2 的删除分数和 deletion AUC 高于 E1，保留证据后的绝对差更低；这说明 E2 的局部 Shapley/遮蔽排序在本批样本上比 Router utility 更贴合预测器的遮蔽响应。两者的平均模态排序 Spearman 为 0.725，主导模态一致率为 17/20（85%），Router 与反事实贡献存在一定对应，但并不相同。E0/E2 的尺度稳定性是窗口宽度 1/3/5 得分排序相关的样本均值；E1 不是多尺度遮蔽解释，故不计算该值。
+
+Shapley 完备性检查覆盖分类与强度两种输出，最大绝对残差为 `4.44×10⁻¹⁶`；E1/E2 的 20 个样本预测类别、强度和类别概率逐项一致。上述 faithfulness 数值是模型响应诊断，不是人工解释准确率；删除/保留输入也可能偏离训练时的掩码分布，尤其是仅保留 10% 的情景。附件 4 没有词、音频帧或视频帧的可信时间戳；输出的视频秒数由归一化进程乘视频时长估算，只作人工回看定位，不代表物理时间对齐。遮蔽贡献也不证明现实情绪成因。
+
+完整结果：[Q3 方案汇总](output/q3/first_round/q3_method_comparison.csv) · [运行清单与完备性审计](output/q3/first_round/run_manifest.json) · [附件 4 预测](output/q3/first_round/attachment4_predictions.csv) · [模态 Shapley 贡献](output/q3/first_round/attachment4_modal_shapley.csv) · [跨模态交互](output/q3/first_round/attachment4_pairwise_interactions.csv) · [局部证据](output/q3/first_round/attachment4_local_evidence.csv) · [逐样本 faithfulness](output/q3/first_round/faithfulness_by_sample.csv) · [验证集指标](output/q3/first_round/validation_metrics.json) · [验证集错误归因](output/q3/first_round/validation_error_attribution.csv) · [典型解释卡](output/q3/first_round/typical_explanation_card.md)。全部 20 个样本解释卡、证据图、候选帧与 CSV 保存在 [`output/q3/first_round/`](output/q3/first_round/)。
+
+### MoFE Router 热力图示例
+
+下图选取附件 4 样本 02（完整输入时 MoFE 预测中性）和样本 03（预测负向）。每个样本各展示完整输入与一组受控遮蔽：样本 02 遮蔽文本 bins 6–20 和音频 bins 19–33，样本 03 遮蔽视觉 bins 19–33。完整样本三种模态各有 50/50 个可见 bin；残缺输入是人为构造的对照，不是附件 4 的原生缺失记录。上方文本、波形和视频帧只用蓝色斜线标记被遮蔽区；下方将 T、A、V、TA、TV、AV、TAV 七个 expert 横向并排，色块/数值表示该样本的平均 Router 权重，下方窄条显示 50 个相对进程 bin 上的 α[t,e]。每个 bin 的权重在可用 expert 中归一化；非可用 expert 权重为 0。色条显示原始 0–1 分数并用平方根归一化增强低权重可见度。顶部 T/A/V 数值是跨位置的 Router exposure share。
+
+样本 02 的 T/A/V exposure share 从完整输入的 35.8%/28.9%/35.2% 变为遮蔽文本与音频后的 29.9%/25.9%/44.1%，分类预测从中性变为正向。样本 03 从 35.9%/29.2%/34.9% 变为遮蔽视觉后的 39.6%/34.2%/26.2%，分类仍为负向。这些是两个受控样本的 Router 路由变化示例，不表示缺失模态的真实情绪贡献；α[t,e] 也不是反事实预测贡献。
+
+![附件 4 完整与受控残缺样本的 MoFE Router 热力图](output/q3/mofe_router_heatmap_examples.png)
+
+[PNG 高清图](output/q3/mofe_router_heatmap_examples.png) · [PDF 矢量版](output/q3/mofe_router_heatmap_examples.pdf) · [逐位置 Router 分数](output/q3/mofe_router_heatmap_scores.csv) · [样本与条件摘要](output/q3/mofe_router_heatmap_summary.csv) · [热力图运行清单](output/q3/mofe_router_heatmap_manifest.json) · [可复现绘图脚本](q3/plot_router_heatmaps.py)。
diff --git a/final/output/q3/first_round/attachment4_evidence_segments.csv b/final/output/q3/first_round/attachment4_evidence_segments.csv
new file mode 100644
index 0000000..4e8fcae
--- /dev/null
+++ b/final/output/q3/first_round/attachment4_evidence_segments.csv
@@ -0,0 +1,333 @@
+variant,case_id,modality,rank_within_modality,slot_start_0based,slot_end_exclusive,relative_progress_start,relative_progress_end,source_row_start,source_row_end_exclusive,local_score_sum_signed,local_score_mass,direction,score_semantics,evidence_text,text_evidence_method,source_video,video_time_start_sec_estimate,video_time_end_sec_estimate,video_time_basis,candidate_frame
+E0_EarlyConcat,01,text,1,19,22,0.3799999952316284,0.4399999976158142,9,11,0.21195699274539948,0.21195699274539948,supports_predicted_class,predicted-class logit drop after local occlusion,belt is,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/01.mp4,3.4199999570846558,3.959999978542328,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,01,text,2,47,48,0.9399999976158142,0.9599999785423279,22,24,0.06938863545656204,0.06938863545656204,supports_predicted_class,predicted-class logit drop after local occlusion,requirements,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/01.mp4,8.459999978542328,8.639999806880951,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,01,audio,1,22,25,0.4399999976158142,0.5,78,90,-0.061767300590872765,0.061767300590872765,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 78–89; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/01.mp4,3.959999978542328,4.5,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,01,audio,2,3,5,0.05999999865889549,0.10000000149011612,10,18,-0.038241248577833176,0.038241248577833176,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 10–17; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/01.mp4,0.5399999879300594,0.9000000134110451,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,01,vision,1,46,49,0.9200000166893005,0.9800000190734863,124,133,-0.032375117763876915,0.032375117763876915,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 124–132; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/01.mp4,8.280000150203705,8.820000171661377,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_01_vision_1.jpg
+E0_EarlyConcat,01,vision,2,32,34,0.6399999856948853,0.6800000071525574,86,92,-0.019244452007114887,0.019244452007114887,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 86–91; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/01.mp4,5.759999871253967,6.120000064373016,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_01_vision_2.jpg
+E2_MoFE_Shapley,01,text,1,15,19,0.30000001192092896,0.3799999952316284,7,10,0.32815835624933243,0.32815835624933243,supports_predicted_class,predicted-class logit drop after local occlusion,the timing belt,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/01.mp4,2.7000001072883606,3.4199999570846558,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,01,text,2,32,33,0.6399999856948853,0.6600000262260437,15,16,0.0799548327922821,0.0799548327922821,supports_predicted_class,predicted-class logit drop after local occlusion,new,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/01.mp4,5.759999871253967,5.940000236034393,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,01,audio,1,1,3,0.019999999552965164,0.05999999865889549,3,11,-0.017126997001469135,0.017126997001469135,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 3–10; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/01.mp4,0.17999999597668648,0.5399999879300594,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,01,audio,2,16,18,0.3199999928474426,0.36000001430511475,57,65,-0.015658795833587646,0.015658795833587646,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 57–64; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/01.mp4,2.8799999356269836,3.2400001287460327,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,01,vision,1,5,9,0.10000000149011612,0.18000000715255737,13,25,-0.06888958811759949,0.06888958811759949,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 13–24; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/01.mp4,0.9000000134110451,1.6200000643730164,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_01_vision_1.jpg
+E2_MoFE_Shapley,01,vision,2,46,47,0.9200000166893005,0.9399999976158142,124,127,-0.01270914077758789,0.01270914077758789,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 124–126; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/01.mp4,8.280000150203705,8.459999978542328,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_01_vision_2.jpg
+E1_MoFE_Router,01,text,1,35,37,0.699999988079071,0.7400000095367432,16,18,1.281982421875,1.281982421875,router_activity_not_signed_contribution,internal router utility; not a prediction effect,belt performs,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/01.mp4,6.299999892711639,6.6600000858306885,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,01,text,2,4,5,0.07999999821186066,0.10000000149011612,1,3,0.6409912109375,0.6409912109375,router_activity_not_signed_contribution,internal router utility; not a prediction effect,replacing these,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/01.mp4,0.7199999839067459,0.9000000134110451,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,01,audio,1,32,33,0.6399999856948853,0.6600000262260437,114,119,0.52117919921875,0.52117919921875,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 114–118; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/01.mp4,5.759999871253967,5.940000236034393,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,01,audio,2,35,36,0.699999988079071,0.7200000286102295,125,129,0.52105712890625,0.52105712890625,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 125–128; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/01.mp4,6.299999892711639,6.480000257492065,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,01,vision,1,4,6,0.07999999821186066,0.11999999731779099,10,17,1.21209716796875,1.21209716796875,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 10–16; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/01.mp4,0.7199999839067459,1.0799999758601189,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_01_vision_1.jpg
+E1_MoFE_Router,01,vision,2,35,37,0.699999988079071,0.7400000095367432,94,100,1.21051025390625,1.21051025390625,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 94–99; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/01.mp4,6.299999892711639,6.6600000858306885,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_01_vision_2.jpg
+E0_EarlyConcat,02,text,1,22,27,0.4399999976158142,0.5400000214576721,9,12,0.48395901918411255,0.48395901918411255,supports_predicted_class,predicted-class logit drop after local occlusion,s happiness -,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/02.mp4,1.5369063516721129,1.8862033349509835,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,02,audio,1,35,40,0.699999988079071,0.800000011920929,46,53,0.10358285903930664,0.10358285903930664,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 46–52; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/02.mp4,2.4450782583605646,2.794375241639435,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,02,vision,1,10,13,0.20000000298023224,0.25999999046325684,10,13,-0.08763947151601315,0.08763947151601315,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 10–12; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/02.mp4,0.6985938104098588,0.9081719066884517,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_02_vision_1.jpg
+E0_EarlyConcat,02,vision,2,34,35,0.6800000071525574,0.699999988079071,34,36,-0.02816396951675415,0.02816396951675415,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 34–35; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/02.mp4,2.3752189449836614,2.4450782583605646,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_02_vision_2.jpg
+E2_MoFE_Shapley,02,text,1,26,29,0.5199999809265137,0.5799999833106995,10,13,0.17752646654844284,0.17752646654844284,supports_predicted_class,predicted-class logit drop after local occlusion,happiness - not,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/02.mp4,1.8163438133769034,2.0259219617047908,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,02,text,2,39,40,0.7799999713897705,0.800000011920929,16,17,0.06014342978596687,0.06014342978596687,supports_predicted_class,predicted-class logit drop after local occlusion,’,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/02.mp4,2.724515720065355,2.794375241639435,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,02,audio,1,31,34,0.6200000047683716,0.6800000071525574,40,45,0.04151773452758789,0.04151773452758789,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 40–44; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/02.mp4,2.165640796655774,2.3752189449836614,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,02,audio,2,35,37,0.699999988079071,0.7400000095367432,46,49,0.02676789928227663,0.02676789928227663,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 46–48; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/02.mp4,2.4450782583605646,2.5847970933115483,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,02,vision,1,40,44,0.800000011920929,0.8799999952316284,40,44,-0.0535764005035162,0.0535764005035162,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 40–43; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/02.mp4,2.794375241639435,3.0738127033442257,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_02_vision_1.jpg
+E2_MoFE_Shapley,02,vision,2,17,18,0.3400000035762787,0.36000001430511475,17,19,0.010036717168986797,0.010036717168986797,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 17–18; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/02.mp4,1.1876094724918307,1.2574688899673223,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_02_vision_2.jpg
+E1_MoFE_Router,02,text,1,0,4,0.0,0.07999999821186066,0,2,2.5281982421875,2.5281982421875,router_activity_not_signed_contribution,internal router utility; not a prediction effect,we,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/02.mp4,0.0,0.2794375137540847,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,02,text,2,48,49,0.9599999785423279,0.9800000190734863,20,21,0.6318359375,0.6318359375,router_activity_not_signed_contribution,internal router utility; not a prediction effect,We want to live by each other’s happiness - not by each other’s misery.,whole_transcript_special_tokens_only,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/02.mp4,3.3532501650490163,3.4231096866230963,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,02,audio,1,19,21,0.3799999952316284,0.41999998688697815,25,28,1.02581787109375,1.02581787109375,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 25–27; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/02.mp4,1.327328203344226,1.4670469341966212,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,02,audio,2,9,11,0.18000000715255737,0.2199999988079071,11,15,1.02532958984375,1.02532958984375,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 11–14; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/02.mp4,0.6287344449836612,0.7684531758360564,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,02,vision,1,48,50,0.9599999785423279,1.0,48,50,1.24761962890625,1.24761962890625,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 48–49; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/02.mp4,3.3532501650490163,3.492969,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_02_vision_1.jpg
+E1_MoFE_Router,02,vision,2,1,3,0.019999999552965164,0.05999999865889549,1,3,1.246826171875,1.246826171875,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 1–2; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/02.mp4,0.06985937843852118,0.20957813531556352,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_02_vision_2.jpg
+E0_EarlyConcat,03,text,1,30,35,0.6000000238418579,0.699999988079071,21,25,0.6632056906819344,0.6632056906819344,supports_predicted_class,predicted-class logit drop after local occlusion,take rental income into,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/03.mp4,5.7983984304076435,6.764797784796179,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,03,audio,1,14,18,0.2800000011920929,0.36000001430511475,53,69,0.0951934065669775,0.0951934065669775,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 53–68; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/03.mp4,2.705919171520382,3.4790390582445863,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,03,audio,2,48,49,0.9599999785423279,0.9800000190734863,183,188,0.02078845165669918,0.02078845165669918,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 183–187; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/03.mp4,9.27743691263312,9.470717244326115,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,03,vision,1,17,20,0.3400000035762787,0.4000000059604645,48,58,-0.07836657762527466,0.07836657762527466,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 48–57; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/03.mp4,3.2857590145611466,3.8655988576019107,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_03_vision_1.jpg
+E0_EarlyConcat,03,vision,2,2,3,0.03999999910593033,0.05999999865889549,5,9,-0.025225579738616943,0.025225579738616943,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 5–8; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/03.mp4,0.38655987135971337,0.57983980703957,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_03_vision_2.jpg
+E2_MoFE_Shapley,03,text,1,44,46,0.8799999952316284,0.9200000166893005,30,33,0.26579518616199493,0.26579518616199493,supports_predicted_class,predicted-class logit drop after local occlusion,yield into account,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/03.mp4,8.504317313918472,8.890877401285351,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,03,text,2,35,37,0.699999988079071,0.7400000095367432,24,26,0.26180368661880493,0.26180368661880493,supports_predicted_class,predicted-class logit drop after local occlusion,into account,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/03.mp4,6.764797784796179,7.151357872163057,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,03,audio,1,7,11,0.14000000059604645,0.2199999988079071,26,43,-0.12540102005004883,0.12540102005004883,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 26–42; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/03.mp4,1.352959585760191,2.126079328479618,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,03,audio,2,3,4,0.05999999865889549,0.07999999821186066,11,16,-0.02344767190515995,0.02344767190515995,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 11–15; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/03.mp4,0.57983980703957,0.7731197427194267,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,03,vision,1,42,45,0.8399999737739563,0.8999999761581421,120,130,0.09252412989735603,0.09252412989735603,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 120–129; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/03.mp4,8.117757226551593,8.697597069592357,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_03_vision_1.jpg
+E2_MoFE_Shapley,03,vision,2,7,9,0.14000000059604645,0.18000000715255737,20,26,0.05589953996241093,0.05589953996241093,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 20–25; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/03.mp4,1.352959585760191,1.7395195291222931,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_03_vision_2.jpg
+E1_MoFE_Router,03,text,1,33,35,0.6600000262260437,0.699999988079071,23,25,1.26953125,1.26953125,router_activity_not_signed_contribution,internal router utility; not a prediction effect,income into,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/03.mp4,6.3782382734484075,6.764797784796179,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,03,text,2,9,10,0.18000000715255737,0.20000000298023224,6,7,0.6348876953125,0.6348876953125,router_activity_not_signed_contribution,internal router utility; not a prediction effect,##er,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/03.mp4,1.7395195291222931,1.9327994288009553,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,03,audio,1,31,35,0.6200000047683716,0.699999988079071,118,134,2.06378173828125,2.06378173828125,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 118–133; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/03.mp4,5.991678186081529,6.764797784796179,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,03,audio,2,0,1,0.0,0.019999999552965164,0,4,0.51568603515625,0.51568603515625,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 0–3; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/03.mp4,0.0,0.19327993567985668,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,03,vision,1,37,40,0.7400000095367432,0.800000011920929,106,116,1.85589599609375,1.85589599609375,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 106–115; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/03.mp4,7.151357872163057,7.731197715203821,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_03_vision_1.jpg
+E1_MoFE_Router,03,vision,2,48,49,0.9599999785423279,0.9800000190734863,138,142,0.6190185546875,0.6190185546875,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 138–141; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/03.mp4,9.27743691263312,9.470717244326115,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_03_vision_2.jpg
+E0_EarlyConcat,04,text,1,5,7,0.10000000149011612,0.14000000059604645,2,4,0.35671761631965637,0.35671761631965637,supports_predicted_class,predicted-class logit drop after local occlusion,i blow,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/04.mp4,0.6994010104218871,0.9791614041687549,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,04,text,2,44,46,0.8799999952316284,0.9200000166893005,24,26,0.3552183657884598,0.3552183657884598,supports_predicted_class,predicted-class logit drop after local occlusion,absolutely not,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/04.mp4,6.154728766649962,6.434489316725135,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,04,audio,1,11,15,0.2199999988079071,0.30000001192092896,30,42,-0.11943121813237667,0.11943121813237667,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 30–41; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/04.mp4,1.5386821916624904,2.0982030833750964,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,04,audio,2,37,38,0.7400000095367432,0.7599999904632568,101,105,-0.0299887266010046,0.0299887266010046,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 101–104; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/04.mp4,5.175567466700077,5.315447533299923,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,04,vision,1,35,40,0.699999988079071,0.800000011920929,72,83,-0.1105031967163086,0.1105031967163086,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 72–82; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/04.mp4,4.895806916624904,5.595208083375097,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_04_vision_1.jpg
+E2_MoFE_Shapley,04,text,1,43,46,0.8600000143051147,0.9200000166893005,24,26,0.41044581681489944,0.41044581681489944,supports_predicted_class,predicted-class logit drop after local occlusion,absolutely not,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/04.mp4,6.014848700050115,6.434489316725135,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,04,text,2,6,8,0.11999999731779099,0.1599999964237213,3,5,0.24217049777507782,0.24217049777507782,supports_predicted_class,predicted-class logit drop after local occlusion,blow it,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/04.mp4,0.8392811812406034,1.1190415749874711,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,04,audio,1,39,43,0.7799999713897705,0.8600000143051147,106,118,-0.11652672477066517,0.11652672477066517,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 106–117; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/04.mp4,5.4553275998997695,6.014848700050115,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,04,audio,2,12,13,0.23999999463558197,0.25999999046325684,32,36,0.023214101791381836,0.023214101791381836,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 32–35; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/04.mp4,1.6785623624812067,1.818442533299923,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,04,vision,1,39,42,0.7799999713897705,0.8399999737739563,80,87,0.23887880891561508,0.23887880891561508,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 80–86; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/04.mp4,5.4553275998997695,5.874968216574788,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_04_vision_1.jpg
+E2_MoFE_Shapley,04,vision,2,31,33,0.6200000047683716,0.6600000262260437,63,68,0.10430653765797615,0.10430653765797615,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 63–67; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/04.mp4,4.336286233350038,4.616046783425212,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_04_vision_2.jpg
+E1_MoFE_Router,04,text,1,44,48,0.8799999952316284,0.9599999785423279,24,27,2.5430908203125,2.5430908203125,router_activity_not_signed_contribution,internal router utility; not a prediction effect,absolutely not.,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/04.mp4,6.154728766649962,6.714249449924827,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,04,text,2,21,22,0.41999998688697815,0.4399999976158142,11,13,0.635009765625,0.635009765625,router_activity_not_signed_contribution,internal router utility; not a prediction effect,i kiss,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/04.mp4,2.937484108287394,3.077364383324981,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,04,audio,1,13,16,0.25999999046325684,0.3199999928474426,35,44,1.549072265625,1.549072265625,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 35–43; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/04.mp4,1.818442533299923,2.2380831499749423,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,04,audio,2,46,47,0.9200000166893005,0.9399999976158142,126,129,0.51629638671875,0.51629638671875,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 126–128; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/04.mp4,6.434489316725135,6.574369383324981,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,04,vision,1,39,43,0.7799999713897705,0.8600000143051147,80,89,2.47479248046875,2.47479248046875,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 80–88; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/04.mp4,5.4553275998997695,6.014848700050115,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_04_vision_1.jpg
+E1_MoFE_Router,04,vision,2,7,8,0.14000000059604645,0.1599999964237213,14,17,0.61773681640625,0.61773681640625,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 14–16; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/04.mp4,0.9791614041687549,1.1190415749874711,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_04_vision_2.jpg
+E0_EarlyConcat,05,text,1,0,3,0.0,0.05999999865889549,0,2,0.15799430757761002,0.15799430757761002,supports_predicted_class,predicted-class logit drop after local occlusion,hi,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/05.mp4,0.0,0.36599999181926246,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,05,text,2,6,8,0.11999999731779099,0.1599999964237213,2,3,0.08240564912557602,0.08240564912557602,supports_predicted_class,predicted-class logit drop after local occlusion,",",bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/05.mp4,0.7319999836385249,0.9759999781847,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,05,audio,1,18,21,0.36000001430511475,0.41999998688697815,43,51,0.10840904526412487,0.10840904526412487,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 43–50; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/05.mp4,2.1960000872611998,2.5619999200105665,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,05,audio,2,40,42,0.800000011920929,0.8399999737739563,96,101,0.05642612650990486,0.05642612650990486,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 96–100; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/05.mp4,4.8800000727176664,5.123999840021133,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,05,vision,1,21,25,0.41999998688697815,0.5,37,45,0.2373512201011181,0.2373512201011181,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 37–44; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/05.mp4,2.5619999200105665,3.05,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_05_vision_1.jpg
+E0_EarlyConcat,05,vision,2,44,45,0.8799999952316284,0.8999999761581421,79,81,0.05083874985575676,0.05083874985575676,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 79–80; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/05.mp4,5.367999970912933,5.489999854564666,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_05_vision_2.jpg
+E2_MoFE_Shapley,05,text,1,6,11,0.11999999731779099,0.2199999988079071,2,4,0.2561522349715233,0.2561522349715233,supports_predicted_class,predicted-class logit drop after local occlusion,", my",bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/05.mp4,0.7319999836385249,1.3419999927282333,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,05,audio,1,1,6,0.019999999552965164,0.11999999731779099,2,15,-0.15269279666244984,0.15269279666244984,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 2–14; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/05.mp4,0.1219999972730875,0.7319999836385249,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,05,vision,1,43,48,0.8600000143051147,0.9599999785423279,77,87,0.3148336037993431,0.3148336037993431,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 77–86; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/05.mp4,5.2460000872612,5.855999869108199,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_05_vision_1.jpg
+E1_MoFE_Router,05,text,1,43,46,0.8600000143051147,0.9200000166893005,14,16,1.901611328125,1.901611328125,router_activity_not_signed_contribution,internal router utility; not a prediction effect,marketing.,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/05.mp4,5.2460000872612,5.612000101804733,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,05,text,2,0,1,0.0,0.019999999552965164,0,1,0.6363525390625,0.6363525390625,router_activity_not_signed_contribution,internal router utility; not a prediction effect,"Hi, my name is Chloe, video marketer for Red Wagon Marketing.",whole_transcript_special_tokens_only,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/05.mp4,0.0,0.1219999972730875,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,05,audio,1,43,47,0.8600000143051147,0.9399999976158142,103,113,2.06158447265625,2.06158447265625,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 103–112; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/05.mp4,5.2460000872612,5.7339999854564665,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,05,audio,2,23,24,0.46000000834465027,0.47999998927116394,55,58,0.51544189453125,0.51544189453125,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 55–57; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/05.mp4,2.8060000509023664,2.9279999345540997,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,05,vision,1,3,6,0.05999999865889549,0.11999999731779099,5,11,1.8525390625,1.8525390625,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 5–10; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/05.mp4,0.36599999181926246,0.7319999836385249,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_05_vision_1.jpg
+E1_MoFE_Router,05,vision,2,17,18,0.3400000035762787,0.36000001430511475,30,33,0.61810302734375,0.61810302734375,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 30–32; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/05.mp4,2.0740000218153,2.1960000872611998,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_05_vision_2.jpg
+E0_EarlyConcat,06,text,1,8,11,0.1599999964237213,0.2199999988079071,5,9,0.35889526456594467,0.35889526456594467,supports_predicted_class,predicted-class logit drop after local occlusion,a fan of dancing,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/06.mp4,1.328802050298959,1.8271028500996531,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,06,text,2,21,23,0.41999998688697815,0.46000000834465027,15,18,0.21360023319721222,0.21360023319721222,supports_predicted_class,predicted-class logit drop after local occlusion,to watch people,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/06.mp4,3.4881053510961832,3.8203060493024292,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,06,audio,1,5,9,0.10000000149011612,0.18000000715255737,16,30,-0.10391966439783573,0.10391966439783573,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 16–29; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/06.mp4,0.8305013123754338,1.494902399402082,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,06,audio,2,38,39,0.7599999904632568,0.7799999713897705,123,128,-0.023784717544913292,0.023784717544913292,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 123–127; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/06.mp4,6.311809800797224,6.477909902391673,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,06,vision,1,33,37,0.6600000262260437,0.7400000095367432,81,92,-0.08071585558354855,0.08071585558354855,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 81–91; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/06.mp4,5.4813087978076345,6.145709699202777,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_06_vision_1.jpg
+E0_EarlyConcat,06,vision,2,40,41,0.800000011920929,0.8199999928474426,98,101,-0.01990342140197754,0.01990342140197754,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 98–100; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/06.mp4,6.64401049900347,6.810110600597919,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_06_vision_2.jpg
+E2_MoFE_Shapley,06,text,1,21,24,0.41999998688697815,0.47999998927116394,15,18,0.45087654888629913,0.45087654888629913,supports_predicted_class,predicted-class logit drop after local occlusion,to watch people,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/06.mp4,3.4881053510961832,3.9864061508968773,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,06,text,2,7,9,0.14000000059604645,0.18000000715255737,5,7,0.27771830558776855,0.27771830558776855,supports_predicted_class,predicted-class logit drop after local occlusion,a fan,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/06.mp4,1.1627018249501735,1.494902399402082,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,06,audio,1,42,45,0.8399999737739563,0.8999999761581421,136,147,-0.12066813558340073,0.12066813558340073,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 136–146; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/06.mp4,6.9762107021923665,7.474511501993061,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,06,audio,2,14,16,0.2800000011920929,0.3199999928474426,45,53,-0.07646659761667252,0.07646659761667252,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 45–52; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/06.mp4,2.325403649900347,2.657604100597918,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,06,vision,1,40,45,0.800000011920929,0.8999999761581421,98,111,-0.10251033492386341,0.10251033492386341,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 98–110; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/06.mp4,6.64401049900347,7.474511501993061,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_06_vision_1.jpg
+E1_MoFE_Router,06,text,1,40,41,0.800000011920929,0.8199999928474426,29,31,0.633056640625,0.633056640625,router_activity_not_signed_contribution,internal router utility; not a prediction effect,check out,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/06.mp4,6.64401049900347,6.810110600597919,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,06,text,2,0,1,0.0,0.019999999552965164,0,1,0.6329345703125,0.6329345703125,router_activity_not_signed_contribution,internal router utility; not a prediction effect,"If you're a fan of dancing in that sense, just like to watch people dance, see impressive dance moves then you might want to check out this movie solely for that",whole_transcript_special_tokens_only,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/06.mp4,0.0,0.16610025628736988,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,06,audio,1,43,45,0.8600000143051147,0.8999999761581421,140,147,1.0257568359375,1.0257568359375,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 140–146; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/06.mp4,7.142311298804165,7.474511501993061,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,06,audio,2,39,41,0.7799999713897705,0.8199999928474426,127,134,1.025634765625,1.025634765625,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 127–133; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/06.mp4,6.477909902391673,6.810110600597919,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,06,vision,1,46,50,0.9200000166893005,1.0,113,123,2.48345947265625,2.48345947265625,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 113–122; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/06.mp4,7.6406120986048585,8.305013,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_06_vision_1.jpg
+E1_MoFE_Router,06,vision,2,13,14,0.25999999046325684,0.2800000011920929,31,35,0.621337890625,0.621337890625,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 31–34; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/06.mp4,2.1593033007972244,2.325403649900347,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_06_vision_2.jpg
+E0_EarlyConcat,07,text,1,23,28,0.46000000834465027,0.5600000023841858,23,28,0.5156635791063309,0.5156635791063309,supports_predicted_class,predicted-class logit drop after local occlusion,the topics of growing the,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/07.mp4,9.820080178141593,11.954880050897598,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,07,audio,1,1,4,0.019999999552965164,0.07999999821186066,8,34,0.05877157114446163,0.05877157114446163,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 8–33; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/07.mp4,0.4269599904567003,1.7078399618268012,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,07,audio,2,11,13,0.2199999988079071,0.25999999046325684,93,111,0.03315412998199463,0.03315412998199463,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 93–110; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/07.mp4,4.6965599745512,5.550479796409607,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,07,vision,1,10,14,0.20000000298023224,0.2800000011920929,63,90,0.09661082364618778,0.09661082364618778,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 63–89; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/07.mp4,4.269600063621998,5.977440025448799,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_07_vision_1.jpg
+E0_EarlyConcat,07,vision,2,4,5,0.07999999821186066,0.10000000149011612,25,32,0.017936071380972862,0.017936071380972862,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 25–31; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/07.mp4,1.7078399618268012,2.134800031810999,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_07_vision_2.jpg
+E2_MoFE_Shapley,07,text,1,22,27,0.4399999976158142,0.5400000214576721,22,27,0.43111030757427216,0.43111030757427216,supports_predicted_class,predicted-class logit drop after local occlusion,discussed the topics of growing,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/07.mp4,9.3931199491024,11.527920458078384,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,07,audio,1,25,27,0.5,0.5400000214576721,212,230,-0.04944984056055546,0.04944984056055546,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 212–229; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/07.mp4,10.674,11.527920458078384,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,07,audio,2,30,32,0.6000000238418579,0.6399999856948853,255,272,-0.04653453826904297,0.04653453826904297,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 255–271; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/07.mp4,12.808800508975983,13.66271969461441,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,07,vision,1,38,42,0.7599999904632568,0.8399999737739563,242,268,-0.10332179255783558,0.10332179255783558,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 242–267; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/07.mp4,16.224479796409607,17.932319440126417,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_07_vision_1.jpg
+E2_MoFE_Shapley,07,vision,2,33,34,0.6600000262260437,0.6800000071525574,210,217,-0.024214783683419228,0.024214783683419228,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 210–216; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/07.mp4,14.08968055987358,14.516640152692794,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_07_vision_2.jpg
+E1_MoFE_Router,07,text,1,31,33,0.6200000047683716,0.6600000262260437,31,33,1.28271484375,1.28271484375,router_activity_not_signed_contribution,internal router utility; not a prediction effect,future of,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/07.mp4,13.235760101795195,14.08968055987358,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,07,text,2,0,1,0.0,0.019999999552965164,0,1,0.6417236328125,0.6417236328125,router_activity_not_signed_contribution,internal router utility; not a prediction effect,"As Linn’s associate editor Michael Baadke reports in our November 28 issue, attendees “enthusiastically discussed the topics of growing the hobby, the future of stamp shows, and dealers and philatelic partnerships, along with ways the leading organizations involved in the stamp hobby can work together to make it succeed and grow",whole_transcript_special_tokens_only,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/07.mp4,0.0,0.4269599904567003,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,07,audio,1,28,30,0.5600000023841858,0.6000000238418579,238,255,1.0413818359375,1.0413818359375,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 238–254; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/07.mp4,11.954880050897598,12.808800508975983,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,07,audio,2,34,36,0.6800000071525574,0.7200000286102295,289,306,1.04034423828125,1.04034423828125,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 289–305; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/07.mp4,14.516640152692794,15.370560610771179,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,07,vision,1,18,20,0.36000001430511475,0.4000000059604645,114,128,1.22320556640625,1.22320556640625,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 114–127; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/07.mp4,7.685280305385589,8.539200127243996,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_07_vision_1.jpg
+E1_MoFE_Router,07,vision,2,36,38,0.7200000286102295,0.7599999904632568,229,243,1.22210693359375,1.22210693359375,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 229–242; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/07.mp4,15.370560610771179,16.224479796409607,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_07_vision_2.jpg
+E0_EarlyConcat,08,text,1,38,41,0.7599999904632568,0.8199999928474426,9,11,0.35957229137420654,0.35957229137420654,supports_predicted_class,predicted-class logit drop after local occlusion,design grand,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/08.mp4,3.8653622314960954,4.170522423622072,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,08,text,2,20,21,0.4000000059604645,0.41999998688697815,5,6,0.11088132858276367,0.11088132858276367,supports_predicted_class,predicted-class logit drop after local occlusion,tonight,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/08.mp4,2.0344012303149404,2.1361211933071313,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,08,audio,1,40,42,0.800000011920929,0.8399999737739563,78,83,-0.02861309051513672,0.02861309051513672,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 78–82; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/08.mp4,4.068802460629881,4.272242386614263,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,08,audio,2,11,13,0.2199999988079071,0.25999999046325684,21,26,0.02798012923449278,0.02798012923449278,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 21–25; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/08.mp4,1.118920653937012,1.3223607314960957,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,08,vision,1,41,44,0.8199999928474426,0.8799999952316284,60,66,-0.041410207748413086,0.041410207748413086,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 60–65; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/08.mp4,4.170522423622072,4.475682615748048,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_08_vision_1.jpg
+E0_EarlyConcat,08,vision,2,3,5,0.05999999865889549,0.10000000149011612,4,8,-0.02705029584467411,0.02705029584467411,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 4–7; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/08.mp4,0.30516017317913846,0.5086003075787351,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_08_vision_2.jpg
+E2_MoFE_Shapley,08,text,1,10,14,0.20000000298023224,0.2800000011920929,2,4,0.316143237054348,0.316143237054348,supports_predicted_class,predicted-class logit drop after local occlusion,brings us,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/08.mp4,1.0172006151574702,1.424080846062988,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,08,text,2,17,18,0.3400000035762787,0.36000001430511475,4,5,0.0770115852355957,0.0770115852355957,supports_predicted_class,predicted-class logit drop after local occlusion,to,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/08.mp4,1.729241038188964,1.8309611527558565,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,08,audio,1,46,49,0.9200000166893005,0.9800000190734863,90,97,-0.12180622294545174,0.12180622294545174,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 90–96; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/08.mp4,4.6791228448818325,4.984283037007809,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,08,audio,2,42,44,0.8399999737739563,0.8799999952316284,82,87,-0.0827360562980175,0.0827360562980175,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 82–86; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/08.mp4,4.272242386614263,4.475682615748048,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,08,vision,1,23,27,0.46000000834465027,0.5400000214576721,34,40,-0.11863954737782478,0.11863954737782478,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 34–39; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/08.mp4,2.3395614224409162,2.746441729133785,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_08_vision_1.jpg
+E2_MoFE_Shapley,08,vision,2,43,44,0.8600000143051147,0.8799999952316284,63,66,-0.01590188406407833,0.01590188406407833,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 63–65; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/08.mp4,4.373962652755856,4.475682615748048,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_08_vision_2.jpg
+E1_MoFE_Router,08,text,1,42,44,0.8399999737739563,0.8799999952316284,10,12,1.2840576171875,1.2840576171875,router_activity_not_signed_contribution,internal router utility; not a prediction effect,grand challenge,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/08.mp4,4.272242386614263,4.475682615748048,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,08,text,2,47,49,0.9399999976158142,0.9800000190734863,12,13,1.28369140625,1.28369140625,router_activity_not_signed_contribution,internal router utility; not a prediction effect,"That brings us to tonight, the Universal Design Grand Challenge",whole_transcript_special_tokens_only,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/08.mp4,4.7808428078740235,4.984283037007809,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,08,audio,1,23,28,0.46000000834465027,0.5600000023841858,45,55,2.603759765625,2.603759765625,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 45–54; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/08.mp4,2.3395614224409162,2.848161692125976,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,08,vision,1,47,50,0.9399999976158142,1.0,69,74,1.8212890625,1.8212890625,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 69–73; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/08.mp4,4.7808428078740235,5.086003,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_08_vision_1.jpg
+E1_MoFE_Router,08,vision,2,42,44,0.8399999737739563,0.8799999952316284,62,66,1.2158203125,1.2158203125,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 62–65; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/08.mp4,4.272242386614263,4.475682615748048,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_08_vision_2.jpg
+E0_EarlyConcat,09,text,1,28,33,0.5600000023841858,0.6600000262260437,11,14,0.7562256008386612,0.7562256008386612,supports_predicted_class,predicted-class logit drop after local occlusion,"at all,",bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/09.mp4,2.8235155320210454,3.327714852231502,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,09,audio,1,28,33,0.5600000023841858,0.6600000262260437,55,66,-0.10792462155222893,0.10792462155222893,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 55–65; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/09.mp4,2.8235155320210454,3.327714852231502,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,09,vision,1,6,11,0.11999999731779099,0.2199999988079071,8,17,-0.16160853765904903,0.16160853765904903,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 8–16; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/09.mp4,0.6050390264763236,1.109238233989477,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_09_vision_1.jpg
+E2_MoFE_Shapley,09,text,1,27,31,0.5400000214576721,0.6200000047683716,10,13,0.37862007319927216,0.37862007319927216,supports_predicted_class,predicted-class logit drop after local occlusion,movie at all,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/09.mp4,2.722675788189411,3.126035064042091,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,09,text,2,35,36,0.699999988079071,0.7200000286102295,13,15,0.08794069290161133,0.08794069290161133,supports_predicted_class,predicted-class logit drop after local occlusion,", i",bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/09.mp4,3.5293943398947714,3.6302343842525477,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,09,audio,1,47,50,0.9399999976158142,1.0,93,99,0.025328159797936678,0.025328159797936678,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 93–98; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/09.mp4,4.7394724679789535,5.041992,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,09,audio,2,2,4,0.03999999910593033,0.07999999821186066,3,8,0.015558242332190275,0.015558242332190275,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 3–7; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/09.mp4,0.20167967549210786,0.4033593509842157,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,09,vision,1,11,14,0.2199999988079071,0.2800000011920929,16,21,0.030474981293082237,0.030474981293082237,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 16–20; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/09.mp4,1.109238233989477,1.4117577660105227,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_09_vision_1.jpg
+E2_MoFE_Shapley,09,vision,2,20,22,0.4000000059604645,0.4399999976158142,30,33,0.02094721794128418,0.02094721794128418,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 30–32; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/09.mp4,2.016796830052614,2.218476467978954,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_09_vision_2.jpg
+E1_MoFE_Router,09,text,1,8,11,0.1599999964237213,0.2199999988079071,3,5,1.90087890625,1.90087890625,router_activity_not_signed_contribution,internal router utility; not a prediction effect,##h ),bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/09.mp4,0.8067187019684314,1.109238233989477,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,09,text,2,4,5,0.07999999821186066,0.10000000149011612,1,2,0.633544921875,0.633544921875,router_activity_not_signed_contribution,internal router utility; not a prediction effect,(,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/09.mp4,0.4033593509842157,0.5041992075131535,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,09,audio,1,8,11,0.1599999964237213,0.2199999988079071,15,22,1.544921875,1.544921875,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 15–21; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/09.mp4,0.8067187019684314,1.109238233989477,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,09,audio,2,2,4,0.03999999910593033,0.07999999821186066,3,8,1.0291748046875,1.0291748046875,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 3–7; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/09.mp4,0.20167967549210786,0.4033593509842157,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,09,vision,1,25,28,0.5,0.5600000023841858,37,42,1.85321044921875,1.85321044921875,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 37–41; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/09.mp4,2.520996,2.8235155320210454,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_09_vision_1.jpg
+E1_MoFE_Router,09,vision,2,12,13,0.23999999463558197,0.25999999046325684,18,20,0.61859130859375,0.61859130859375,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 18–19; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/09.mp4,1.2100780529526471,1.3109178719158172,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_09_vision_2.jpg
+E0_EarlyConcat,10,text,1,42,47,0.8399999737739563,0.9399999976158142,26,31,0.7914992868900299,0.7914992868900299,supports_predicted_class,predicted-class logit drop after local occlusion,this one was pretty terrible,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/10.mp4,8.349577899313807,9.343575536301255,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,10,audio,1,3,6,0.05999999865889549,0.11999999731779099,11,24,-0.06940833665430546,0.06940833665430546,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 11–23; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/10.mp4,0.5963984266694561,1.1927968533389122,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,10,audio,2,22,24,0.4399999976158142,0.47999998927116394,85,94,-0.03950941562652588,0.03950941562652588,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 85–93; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/10.mp4,4.373588536301255,4.771187413355649,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,10,vision,1,4,6,0.07999999821186066,0.11999999731779099,11,18,0.036989012733101845,0.036989012733101845,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 11–17; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/10.mp4,0.7951979022259413,1.1927968533389122,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_10_vision_1.jpg
+E0_EarlyConcat,10,vision,2,23,25,0.46000000834465027,0.5,67,74,0.0356092844158411,0.0356092844158411,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 67–73; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/10.mp4,4.572388122945607,4.969987,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_10_vision_2.jpg
+E2_MoFE_Shapley,10,text,1,7,10,0.14000000059604645,0.20000000298023224,4,7,0.3392959460616112,0.3392959460616112,supports_predicted_class,predicted-class logit drop after local occlusion,and you know,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/10.mp4,1.3915963659246862,1.9879948296234309,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,10,text,2,19,20,0.3799999952316284,0.4000000059604645,12,13,0.10917878150939941,0.10917878150939941,supports_predicted_class,predicted-class logit drop after local occlusion,see,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/10.mp4,3.7771900726025103,3.9759896592468618,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,10,audio,1,4,7,0.07999999821186066,0.14000000059604645,15,28,0.03779570385813713,0.03779570385813713,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 15–27; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/10.mp4,0.7951979022259413,1.3915963659246862,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,10,audio,2,16,17,0.3199999928474426,0.3400000035762787,62,67,-0.007277011871337891,0.007277011871337891,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 62–66; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/10.mp4,3.1807916089037653,3.379591195548117,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,10,vision,1,22,27,0.4399999976158142,0.5400000214576721,64,80,0.2946096733212471,0.2946096733212471,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 64–79; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/10.mp4,4.373588536301255,5.367586173288703,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_10_vision_1.jpg
+E1_MoFE_Router,10,text,1,5,8,0.10000000149011612,0.1599999964237213,3,6,1.8963623046875,1.8963623046875,router_activity_not_signed_contribution,internal router utility; not a prediction effect,) and you,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/10.mp4,0.9939974148117154,1.5903958044518827,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,10,text,2,48,49,0.9599999785423279,0.9800000190734863,30,32,0.6322021484375,0.6322021484375,router_activity_not_signed_contribution,internal router utility; not a prediction effect,terrible,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/10.mp4,9.542374826711297,9.741174709589957,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,10,audio,1,5,7,0.10000000149011612,0.14000000059604645,19,28,1.02618408203125,1.02618408203125,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 19–27; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/10.mp4,0.9939974148117154,1.3915963659246862,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,10,audio,2,28,30,0.5600000023841858,0.6000000238418579,109,117,1.02508544921875,1.02508544921875,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 109–116; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/10.mp4,5.566385463698745,5.963984636987448,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,10,vision,1,10,12,0.20000000298023224,0.23999999463558197,29,36,1.238525390625,1.238525390625,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 29–35; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/10.mp4,1.9879948296234309,2.3855937066778243,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_10_vision_1.jpg
+E1_MoFE_Router,10,vision,2,23,24,0.46000000834465027,0.47999998927116394,67,71,0.61895751953125,0.61895751953125,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 67–70; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/10.mp4,4.572388122945607,4.771187413355649,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_10_vision_2.jpg
+E0_EarlyConcat,11,text,1,8,13,0.1599999964237213,0.25999999046325684,2,5,0.781589612364769,0.781589612364769,supports_predicted_class,predicted-class logit drop after local occlusion,would be ashamed,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/11.mp4,0.9286353392433822,1.509032404649019,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,11,audio,1,32,37,0.6399999856948853,0.7400000095367432,72,85,0.28002240508794785,0.28002240508794785,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 72–84; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/11.mp4,3.7145413569735286,4.29493859535098,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,11,vision,1,21,25,0.41999998688697815,0.5,36,43,-0.08146653696894646,0.08146653696894646,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 36–42; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/11.mp4,2.4376677438924013,2.9019855,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_11_vision_1.jpg
+E0_EarlyConcat,11,vision,2,33,34,0.6600000262260437,0.6800000071525574,56,59,-0.017991483211517334,0.017991483211517334,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 56–58; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/11.mp4,3.830621012215197,3.9467003215132355,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_11_vision_2.jpg
+E2_MoFE_Shapley,11,text,1,8,13,0.1599999964237213,0.25999999046325684,2,5,0.6127279326319695,0.6127279326319695,supports_predicted_class,predicted-class logit drop after local occlusion,would be ashamed,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/11.mp4,0.9286353392433822,1.509032404649019,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,11,audio,1,2,5,0.03999999910593033,0.10000000149011612,4,12,0.18129442259669304,0.18129442259669304,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 4–11; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/11.mp4,0.23215883481084554,0.5803971086485907,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,11,audio,2,33,34,0.6600000262260437,0.6800000071525574,75,78,0.05215102434158325,0.05215102434158325,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 75–77; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/11.mp4,3.830621012215197,3.9467003215132355,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,11,vision,1,47,49,0.9399999976158142,0.9800000190734863,80,85,-0.10202791169285774,0.10202791169285774,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 80–84; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/11.mp4,5.455732726162255,5.687891690701961,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_11_vision_1.jpg
+E2_MoFE_Shapley,11,vision,2,4,5,0.07999999821186066,0.10000000149011612,6,9,-0.05162431672215462,0.05162431672215462,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 6–8; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/11.mp4,0.4643176696216911,0.5803971086485907,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_11_vision_2.jpg
+E1_MoFE_Router,11,text,1,31,35,0.6200000047683716,0.699999988079071,9,12,2.5477294921875,2.5477294921875,router_activity_not_signed_contribution,internal router utility; not a prediction effect,film if i,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/11.mp4,3.5984620476754903,4.062779630811274,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,11,text,2,40,41,0.800000011920929,0.8199999928474426,12,14,0.635986328125,0.635986328125,router_activity_not_signed_contribution,internal router utility; not a prediction effect,was a,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/11.mp4,4.643176869188726,4.759256178486765,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,11,audio,1,1,3,0.019999999552965164,0.05999999865889549,2,7,1.0284423828125,1.0284423828125,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 2–6; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/11.mp4,0.11607941740542277,0.3482382522162683,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,11,audio,2,5,6,0.10000000149011612,0.11999999731779099,11,14,0.514892578125,0.514892578125,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 11–13; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/11.mp4,0.5803971086485907,0.6964765044325366,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,11,vision,1,32,34,0.6399999856948853,0.6800000071525574,55,59,1.2470703125,1.2470703125,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 55–58; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/11.mp4,3.7145413569735286,3.9467003215132355,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_11_vision_1.jpg
+E1_MoFE_Router,11,vision,2,7,8,0.14000000059604645,0.1599999964237213,12,14,0.62469482421875,0.62469482421875,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 12–13; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/11.mp4,0.8125559434594363,0.9286353392433822,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_11_vision_2.jpg
+E0_EarlyConcat,12,text,1,26,29,0.5199999809265137,0.5799999833106995,22,26,0.269247330725193,0.269247330725193,supports_predicted_class,predicted-class logit drop after local occlusion,by fault of their,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/12.mp4,6.767278191777706,7.548118042805493,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,12,text,2,45,46,0.8999999761581421,0.9200000166893005,39,41,0.09271303564310074,0.09271303564310074,supports_predicted_class,predicted-class logit drop after local occlusion,is very,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/12.mp4,11.712596989722133,11.972877457194507,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,12,audio,1,8,11,0.1599999964237213,0.2199999988079071,41,57,-0.051615516655147076,0.051615516655147076,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 41–56; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/12.mp4,2.08223947345832,2.863079324486107,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,12,audio,2,24,25,0.47999998927116394,0.5,123,129,-0.014884431846439838,0.014884431846439838,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 123–128; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/12.mp4,6.246718420374959,6.5069985,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,12,vision,1,3,8,0.05999999865889549,0.1599999964237213,11,31,0.11161545850336552,0.11161545850336552,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 11–30; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/12.mp4,0.7808398025468699,2.08223947345832,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_12_vision_1.jpg
+E2_MoFE_Shapley,12,text,1,43,46,0.8600000143051147,0.9200000166893005,37,41,0.23388135433197021,0.23388135433197021,supports_predicted_class,predicted-class logit drop after local occlusion,credit scores is very,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/12.mp4,11.19203760616672,11.972877457194507,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,12,text,2,9,10,0.18000000715255737,0.20000000298023224,7,9,0.07290557771921158,0.07290557771921158,supports_predicted_class,predicted-class logit drop after local occlusion,had good,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/12.mp4,2.3425195530833602,2.6027994387847335,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,12,audio,1,2,6,0.03999999910593033,0.11999999731779099,10,31,-0.1590693388134241,0.1590693388134241,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 10–30; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/12.mp4,0.52055986836458,1.5616796050937398,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,12,audio,2,10,11,0.20000000298023224,0.2199999988079071,51,57,-0.02710878849029541,0.02710878849029541,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 51–56; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/12.mp4,2.6027994387847335,2.863079324486107,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,12,vision,1,10,15,0.20000000298023224,0.30000001192092896,38,58,0.31226932629942894,0.31226932629942894,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 38–57; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/12.mp4,2.6027994387847335,3.9041992551389337,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_12_vision_1.jpg
+E1_MoFE_Router,12,text,1,31,34,0.6200000047683716,0.6800000071525574,27,30,1.9324951171875,1.9324951171875,router_activity_not_signed_contribution,internal router utility; not a prediction effect,", they have",bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/12.mp4,8.068678202055573,8.84951805308336,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,12,text,2,40,42,0.800000011920929,0.8399999737739563,35,37,1.2891845703125,1.2891845703125,router_activity_not_signed_contribution,internal router utility; not a prediction effect,", and",bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/12.mp4,10.411197755138934,10.931757138694346,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,12,audio,1,10,12,0.20000000298023224,0.23999999463558197,51,62,1.04241943359375,1.04241943359375,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 51–61; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/12.mp4,2.6027994387847335,3.1233592101874796,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,12,audio,2,28,29,0.5600000023841858,0.5799999833106995,143,150,0.52008056640625,0.52008056640625,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 143–149; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/12.mp4,7.287838351027786,7.548118042805493,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,12,vision,1,31,34,0.6200000047683716,0.6800000071525574,119,132,1.864990234375,1.864990234375,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 119–131; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/12.mp4,8.068678202055573,8.84951805308336,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_12_vision_1.jpg
+E1_MoFE_Router,12,vision,2,40,42,0.800000011920929,0.8399999737739563,154,163,1.24432373046875,1.24432373046875,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 154–162; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/12.mp4,10.411197755138934,10.931757138694346,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_12_vision_2.jpg
+E0_EarlyConcat,13,text,1,30,33,0.6000000238418579,0.6600000262260437,19,22,0.28444062918424606,0.28444062918424606,supports_predicted_class,predicted-class logit drop after local occlusion,a relationship between,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/13.mp4,5.207401406923533,5.728141547615886,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,13,text,2,26,28,0.5199999809265137,0.5600000023841858,16,18,0.19023734331130981,0.19023734331130981,supports_predicted_class,predicted-class logit drop after local occlusion,i can,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/13.mp4,4.513080874461174,4.860241140692353,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,13,audio,1,24,26,0.47999998927116394,0.5199999809265137,83,90,0.014062921516597271,0.014062921516597271,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 83–89; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/13.mp4,4.16592086688441,4.513080874461174,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,13,audio,2,20,22,0.4000000059604645,0.4399999976158142,69,77,-0.011938770767301321,0.011938770767301321,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 69–76; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/13.mp4,3.471600851730883,3.818760859307647,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,13,vision,1,45,48,0.8999999761581421,0.9599999785423279,16,18,-0.06461588479578495,0.06461588479578495,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 16–17; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/13.mp4,7.811101593076468,8.33184173376882,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_13_vision_1.jpg
+E0_EarlyConcat,13,vision,2,42,44,0.8399999737739563,0.8799999952316284,15,16,-0.03947160579264164,0.03947160579264164,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 15–15; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/13.mp4,7.290361452384115,7.637521718615294,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_13_vision_2.jpg
+E2_MoFE_Shapley,13,text,1,19,22,0.3799999952316284,0.4399999976158142,12,15,0.12002086639404297,0.12002086639404297,supports_predicted_class,predicted-class logit drop after local occlusion,from that data,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/13.mp4,3.2980207186152937,3.818760859307647,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,13,text,2,9,11,0.18000000715255737,0.2199999988079071,5,8,-0.079388577491045,0.079388577491045,opposes_predicted_class,predicted-class logit drop after local occlusion,could take a,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/13.mp4,1.56222042207706,1.9093804296538235,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,13,audio,1,7,10,0.14000000059604645,0.20000000298023224,24,35,-0.14229143038392067,0.14229143038392067,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 24–34; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/13.mp4,1.2150602851730883,1.7358004258654416,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,13,audio,2,26,28,0.5199999809265137,0.5600000023841858,89,97,-0.08555682376027107,0.08555682376027107,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 89–96; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/13.mp4,4.513080874461174,4.860241140692353,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,13,vision,1,19,22,0.3799999952316284,0.4399999976158142,6,8,0.05050241947174072,0.05050241947174072,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 6–7; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/13.mp4,3.2980207186152937,3.818760859307647,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_13_vision_1.jpg
+E2_MoFE_Shapley,13,vision,2,5,6,0.10000000149011612,0.11999999731779099,2,3,0.01717897318303585,0.01717897318303585,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 2–2; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/13.mp4,0.8679002129327208,1.0414802167211026,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_13_vision_2.jpg
+E1_MoFE_Router,13,text,1,3,5,0.05999999865889549,0.10000000149011612,1,4,1.41015625,1.41015625,router_activity_not_signed_contribution,internal router utility; not a prediction effect,"for example,",bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/13.mp4,0.5207401083605513,0.8679002129327208,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,13,text,2,14,16,0.2800000011920929,0.3199999928474426,8,11,1.2652587890625,1.2652587890625,router_activity_not_signed_contribution,internal router utility; not a prediction effect,set of data,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/13.mp4,2.4301205703461766,2.7772805779229404,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,13,audio,1,1,6,0.019999999552965164,0.11999999731779099,3,21,2.89013671875,2.89013671875,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 3–20; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/13.mp4,0.17358003612018377,1.0414802167211026,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,13,vision,1,14,16,0.2800000011920929,0.3199999928474426,5,6,1.24224853515625,1.24224853515625,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 5–5; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/13.mp4,2.4301205703461766,2.7772805779229404,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_13_vision_1.jpg
+E1_MoFE_Router,13,vision,2,20,21,0.4000000059604645,0.41999998688697815,7,8,0.62188720703125,0.62188720703125,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 7–7; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/13.mp4,3.471600851730883,3.6451807261920575,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_13_vision_2.jpg
+E0_EarlyConcat,14,text,1,33,36,0.6600000262260437,0.7200000286102295,24,27,-0.3711981028318405,0.3711981028318405,opposes_predicted_class,predicted-class logit drop after local occlusion,( ufe,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/14.mp4,6.831645091465175,7.452703736143827,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,14,text,2,28,30,0.5600000023841858,0.6000000238418579,20,23,-0.19144849479198456,0.19144849479198456,opposes_predicted_class,predicted-class logit drop after local occlusion,of uniform final,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/14.mp4,5.7965471446786525,6.2105864467865235,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,14,audio,1,25,30,0.5,0.6000000238418579,102,123,0.23154576495289803,0.23154576495289803,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 102–122; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/14.mp4,5.1754885,6.2105864467865235,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,14,vision,1,37,40,0.7400000095367432,0.800000011920929,113,123,0.218707837164402,0.218707837164402,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 113–122; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/14.mp4,7.65972307871461,8.280781723393261,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_14_vision_1.jpg
+E0_EarlyConcat,14,vision,2,29,31,0.5799999833106995,0.6200000047683716,88,95,0.14758924394845963,0.14758924394845963,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 88–94; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/14.mp4,6.003566487249434,6.417605789357305,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_14_vision_2.jpg
+E2_MoFE_Shapley,14,text,1,29,31,0.5799999833106995,0.6200000047683716,21,23,-0.19209232181310654,0.19209232181310654,opposes_predicted_class,predicted-class logit drop after local occlusion,uniform final,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/14.mp4,6.003566487249434,6.417605789357305,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,14,text,2,6,8,0.11999999731779099,0.1599999964237213,4,6,-0.1878821849822998,0.1878821849822998,opposes_predicted_class,predicted-class logit drop after local occlusion,co -,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/14.mp4,1.2421172122365163,1.6561562829820216,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,14,audio,1,32,36,0.6399999856948853,0.7200000286102295,130,147,-0.1527111902832985,0.1527111902832985,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 130–146; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/14.mp4,6.624625131928086,7.452703736143827,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,14,audio,2,15,16,0.30000001192092896,0.3199999928474426,61,66,-0.035477519035339355,0.035477519035339355,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 61–65; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/14.mp4,3.1052932233932617,3.312312565964043,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,14,vision,1,0,3,0.0,0.05999999865889549,0,10,0.22596383094787598,0.22596383094787598,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 0–9; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/14.mp4,0.0,0.6210586061182581,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_14_vision_1.jpg
+E2_MoFE_Shapley,14,vision,2,47,49,0.9399999976158142,0.9800000190734863,143,150,0.11888899654150009,0.11888899654150009,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 143–149; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/14.mp4,9.729918355321349,10.143957657429219,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_14_vision_2.jpg
+E1_MoFE_Router,14,text,1,0,3,0.0,0.05999999865889549,0,3,1.9134521484375,1.9134521484375,router_activity_not_signed_contribution,internal router utility; not a prediction effect,he is,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/14.mp4,0.0,0.6210586061182581,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,14,text,2,46,47,0.9200000166893005,0.9399999976158142,34,35,0.63720703125,0.63720703125,router_activity_not_signed_contribution,internal router utility; not a prediction effect,university,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/14.mp4,9.522899012750566,9.729918355321349,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,14,audio,1,0,3,0.0,0.05999999865889549,0,13,1.555908203125,1.555908203125,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 0–12; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/14.mp4,0.0,0.6210586061182581,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,14,audio,2,46,47,0.9200000166893005,0.9399999976158142,187,192,0.51788330078125,0.51788330078125,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 187–191; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/14.mp4,9.522899012750566,9.729918355321349,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,14,vision,1,5,8,0.10000000149011612,0.1599999964237213,15,25,1.84735107421875,1.84735107421875,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 15–24; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/14.mp4,1.0350977154241576,1.6561562829820216,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_14_vision_1.jpg
+E1_MoFE_Router,14,vision,2,32,33,0.6399999856948853,0.6600000262260437,97,101,0.6156005859375,0.6156005859375,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 97–100; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/14.mp4,6.624625131928086,6.831645091465175,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_14_vision_2.jpg
+E0_EarlyConcat,15,text,1,20,23,0.4000000059604645,0.46000000834465027,9,11,0.13611793518066406,0.13611793518066406,supports_predicted_class,predicted-class logit drop after local occlusion,prioriti,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/15.mp4,2.9333332437100705,3.373333241194099,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,15,text,2,41,43,0.8199999928474426,0.8600000143051147,18,20,0.08169221878051758,0.08169221878051758,supports_predicted_class,predicted-class logit drop after local occlusion,power to,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/15.mp4,6.0133330075479146,6.30666648490417,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,15,audio,1,3,6,0.05999999865889549,0.11999999731779099,8,18,0.03540682792663574,0.03540682792663574,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 8–17; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/15.mp4,0.439999970165234,0.879999940330468,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,15,audio,2,11,12,0.2199999988079071,0.23999999463558197,31,35,0.01048715878278017,0.01048715878278017,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 31–34; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/15.mp4,1.6133332512579857,1.759999880660936,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,15,vision,1,14,17,0.2800000011920929,0.3400000035762787,30,38,0.03825783729553223,0.03825783729553223,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 30–37; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/15.mp4,2.053333248742014,2.4933332462260425,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_15_vision_1.jpg
+E0_EarlyConcat,15,vision,2,49,50,0.9800000190734863,1.0,107,110,0.01098648738116026,0.01098648738116026,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 107–109; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/15.mp4,7.186666479872226,7.333333,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_15_vision_2.jpg
+E2_MoFE_Shapley,15,text,1,40,43,0.800000011920929,0.8600000143051147,18,20,0.19468311965465546,0.19468311965465546,supports_predicted_class,predicted-class logit drop after local occlusion,power to,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/15.mp4,5.866666487420141,6.30666648490417,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,15,text,2,11,12,0.2199999988079071,0.23999999463558197,5,6,-0.06677786260843277,0.06677786260843277,opposes_predicted_class,predicted-class logit drop after local occlusion,poverty,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/15.mp4,1.6133332512579857,1.759999880660936,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,15,audio,1,47,50,0.9399999976158142,1.0,136,145,0.02613520622253418,0.02613520622253418,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 136–144; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/15.mp4,6.893333002515972,7.333333,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,15,audio,2,13,15,0.25999999046325684,0.30000001192092896,37,44,0.017042001243680716,0.017042001243680716,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 37–43; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/15.mp4,1.9066665100638867,2.1999999874201417,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,15,vision,1,11,16,0.2199999988079071,0.3199999928474426,24,36,0.22359855845570564,0.22359855845570564,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 24–35; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/15.mp4,1.6133332512579857,2.3466665075479147,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_15_vision_1.jpg
+E1_MoFE_Router,15,text,1,15,17,0.30000001192092896,0.3400000035762787,6,8,1.2694091796875,1.2694091796875,router_activity_not_signed_contribution,internal router utility; not a prediction effect,", the",bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/15.mp4,2.1999999874201417,2.4933332462260425,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,15,text,2,27,29,0.5400000214576721,0.5799999833106995,12,14,1.26904296875,1.26904296875,router_activity_not_signed_contribution,internal router utility; not a prediction effect,education because,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/15.mp4,3.959999977356255,4.253333017611801,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,15,audio,1,8,10,0.1599999964237213,0.20000000298023224,23,29,1.0308837890625,1.0308837890625,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 23–28; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/15.mp4,1.1733332537739574,1.4666666218550353,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,15,audio,2,15,16,0.30000001192092896,0.3199999928474426,43,47,0.515625,0.515625,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 43–46; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/15.mp4,2.1999999874201417,2.3466665075479147,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,15,vision,1,47,49,0.9399999976158142,0.9800000190734863,103,108,1.2357177734375,1.2357177734375,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 103–107; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/15.mp4,6.893333002515972,7.186666479872226,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_15_vision_1.jpg
+E1_MoFE_Router,15,vision,2,23,25,0.46000000834465027,0.5,50,55,1.2354736328125,1.2354736328125,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 50–54; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/15.mp4,3.373333241194099,3.6666665,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_15_vision_2.jpg
+E0_EarlyConcat,16,text,1,15,18,0.30000001192092896,0.36000001430511475,3,5,0.47335314750671387,0.47335314750671387,supports_predicted_class,predicted-class logit drop after local occlusion,s a,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/16.mp4,0.934199137121737,1.1210389645460843,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,16,text,2,34,36,0.6800000071525574,0.7200000286102295,8,10,0.29621338844299316,0.29621338844299316,supports_predicted_class,predicted-class logit drop after local occlusion,is a,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/16.mp4,2.1175179822730423,2.2420779290921686,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,16,audio,1,32,35,0.6399999856948853,0.699999988079071,38,43,-0.0550262127071619,0.0550262127071619,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 38–42; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/16.mp4,1.9929580354539156,2.179797862878263,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,16,audio,2,12,14,0.23999999463558197,0.2800000011920929,14,17,-0.0324711799621582,0.0324711799621582,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 14–16; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/16.mp4,0.7473592632952183,0.8719191637121737,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,16,vision,1,9,12,0.18000000715255737,0.23999999463558197,7,11,0.05956967733800411,0.05956967733800411,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 7–10; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/16.mp4,0.5605194822730422,0.7473592632952183,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_16_vision_1.jpg
+E0_EarlyConcat,16,vision,2,47,49,0.9399999976158142,0.9800000190734863,41,44,0.028351306915283203,0.028351306915283203,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 41–43; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/16.mp4,2.9271571725756527,3.051717119394779,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_16_vision_2.jpg
+E2_MoFE_Shapley,16,text,1,43,45,0.8600000143051147,0.8999999761581421,11,12,0.1605234146118164,0.1605234146118164,supports_predicted_class,predicted-class logit drop after local occlusion,movie,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/16.mp4,2.6780374645460845,2.802597225756526,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,16,text,2,15,17,0.30000001192092896,0.3400000035762787,3,5,0.1515650749206543,0.1515650749206543,supports_predicted_class,predicted-class logit drop after local occlusion,s a,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/16.mp4,0.934199137121737,1.0587589911365212,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,16,audio,1,45,50,0.8999999761581421,1.0,54,60,-0.09299127385020256,0.09299127385020256,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 54–59; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/16.mp4,2.802597225756526,3.113997,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,16,vision,1,45,47,0.8999999761581421,0.9399999976158142,39,42,0.020183563232421875,0.020183563232421875,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 39–41; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/16.mp4,2.802597225756526,2.9271571725756527,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_16_vision_1.jpg
+E2_MoFE_Shapley,16,vision,2,31,33,0.6200000047683716,0.6600000262260437,27,30,0.018149375915527344,0.018149375915527344,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 27–29; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/16.mp4,1.9306781548486946,2.0552381016678214,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_16_vision_2.jpg
+E1_MoFE_Router,16,text,1,2,5,0.03999999910593033,0.10000000149011612,0,2,2.109375,2.109375,router_activity_not_signed_contribution,internal router utility; not a prediction effect,it,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/16.mp4,0.12455987721586972,0.3113997046402171,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,16,text,2,46,47,0.9200000166893005,0.9399999976158142,11,13,0.64111328125,0.64111328125,router_activity_not_signed_contribution,internal router utility; not a prediction effect,movie,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/16.mp4,2.8648772919704317,2.9271571725756527,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,16,audio,1,1,6,0.019999999552965164,0.11999999731779099,1,8,3.0616455078125,3.0616455078125,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 1–7; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/16.mp4,0.06227993860793486,0.37367963164760915,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,16,vision,1,27,31,0.5400000214576721,0.6200000047683716,23,28,2.4227294921875,2.4227294921875,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 23–27; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/16.mp4,1.6815584468191265,1.9306781548486946,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_16_vision_1.jpg
+E1_MoFE_Router,16,vision,2,15,16,0.30000001192092896,0.3199999928474426,13,15,0.60540771484375,0.60540771484375,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 13–14; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/16.mp4,0.934199137121737,0.9964790177269578,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_16_vision_2.jpg
+E0_EarlyConcat,17,text,1,26,28,0.5199999809265137,0.5600000023841858,13,15,0.13179675489664078,0.13179675489664078,supports_predicted_class,predicted-class logit drop after local occlusion,and will,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/17.mp4,5.02683565561676,5.413515543047906,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,17,text,2,2,3,0.03999999910593033,0.05999999865889549,1,2,0.06553149223327637,0.06553149223327637,supports_predicted_class,predicted-class logit drop after local occlusion,applying,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/17.mp4,0.3866796713570357,0.5800195070355535,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,17,audio,1,32,34,0.6399999856948853,0.6800000071525574,122,130,-0.010544061660766602,0.010544061660766602,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 122–129; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/17.mp4,6.186874741712571,6.5735546291437155,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,17,audio,2,2,4,0.03999999910593033,0.07999999821186066,7,16,-0.009335438255220652,0.009335438255220652,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 7–15; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/17.mp4,0.3866796713570357,0.7733593427140714,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,17,vision,1,46,50,0.9200000166893005,1.0,131,143,0.022278706543147564,0.022278706543147564,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 131–142; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/17.mp4,8.893632801335336,9.666992,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_17_vision_1.jpg
+E0_EarlyConcat,17,vision,2,29,30,0.5799999833106995,0.6000000238418579,82,86,0.004997968673706055,0.004997968673706055,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 82–85; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/17.mp4,5.6068551986646655,5.80019543047905,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_17_vision_2.jpg
+E2_MoFE_Shapley,17,text,1,29,32,0.5799999833106995,0.6399999856948853,15,17,0.30716411769390106,0.30716411769390106,supports_predicted_class,predicted-class logit drop after local occlusion,make people,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/17.mp4,5.6068551986646655,6.186874741712571,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,17,text,2,21,23,0.41999998688697815,0.46000000834465027,10,12,0.20809631049633026,0.20809631049633026,supports_predicted_class,predicted-class logit drop after local occlusion,"simple,",bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/17.mp4,4.060136513236523,4.446816400667668,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,17,audio,1,46,49,0.9200000166893005,0.9800000190734863,175,188,-0.10685762017965317,0.10685762017965317,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 175–187; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/17.mp4,8.893632801335336,9.47365234438324,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,17,audio,2,11,13,0.2199999988079071,0.25999999046325684,42,50,-0.06556979939341545,0.06556979939341545,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 42–49; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/17.mp4,2.1267382284760474,2.51341782780838,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,17,vision,1,30,33,0.6000000238418579,0.6600000262260437,85,95,0.06669735908508301,0.06669735908508301,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 85–94; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/17.mp4,5.80019543047905,6.380214973526955,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_17_vision_1.jpg
+E2_MoFE_Shapley,17,vision,2,4,6,0.07999999821186066,0.11999999731779099,11,18,0.040630340576171875,0.040630340576171875,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 11–17; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/17.mp4,0.7733593427140714,1.160039014071107,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_17_vision_2.jpg
+E1_MoFE_Router,17,text,1,10,12,0.20000000298023224,0.23999999463558197,5,7,1.276123046875,1.276123046875,router_activity_not_signed_contribution,internal router utility; not a prediction effect,concepts to,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/17.mp4,1.9333984288098813,2.320078028142214,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,17,text,2,48,50,0.9599999785423279,1.0,24,26,1.2760009765625,1.2760009765625,router_activity_not_signed_contribution,internal router utility; not a prediction effect,.,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/17.mp4,9.280312112568856,9.666992,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,17,audio,1,37,39,0.7400000095367432,0.7799999713897705,141,149,1.0296630859375,1.0296630859375,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 141–148; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/17.mp4,7.15357417219162,7.540253483425141,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,17,audio,2,2,3,0.03999999910593033,0.05999999865889549,7,12,0.513916015625,0.513916015625,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 7–11; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/17.mp4,0.3866796713570357,0.5800195070355535,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,17,vision,1,46,48,0.9200000166893005,0.9599999785423279,131,138,1.248291015625,1.248291015625,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 131–137; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/17.mp4,8.893632801335336,9.280312112568856,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_17_vision_1.jpg
+E1_MoFE_Router,17,vision,2,17,19,0.3400000035762787,0.3799999952316284,48,55,1.2471923828125,1.2471923828125,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 48–54; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/17.mp4,3.2867773145718577,3.6734569139041904,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_17_vision_2.jpg
+E0_EarlyConcat,18,text,1,6,11,0.11999999731779099,0.2199999988079071,5,11,0.23141461610794067,0.23141461610794067,supports_predicted_class,predicted-class logit drop after local occlusion,- the first baltic cod fishery,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/18.mp4,2.2569609095529914,4.137761737579107,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,18,audio,1,13,15,0.25999999046325684,0.30000001192092896,96,112,-0.040022531524300575,0.040022531524300575,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 96–111; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/18.mp4,4.890081900632858,5.642402624208928,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,18,audio,2,1,3,0.019999999552965164,0.05999999865889549,7,23,-0.03493002988398075,0.03493002988398075,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 7–22; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/18.mp4,0.37616015159216526,1.1284804547764957,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,18,vision,1,18,21,0.36000001430511475,0.41999998688697815,100,118,0.050717126578092575,0.050717126578092575,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 100–117; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/18.mp4,6.770883149050713,7.899363113370181,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_18_vision_1.jpg
+E0_EarlyConcat,18,vision,2,40,42,0.800000011920929,0.8399999737739563,224,236,0.033449601382017136,0.033449601382017136,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 224–235; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/18.mp4,15.046406624208927,15.798726226740362,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_18_vision_2.jpg
+E2_MoFE_Shapley,18,text,1,3,7,0.05999999865889549,0.14000000059604645,3,7,-0.48844245821237564,0.48844245821237564,opposes_predicted_class,predicted-class logit drop after local occlusion,in denmark - the,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/18.mp4,1.1284804547764957,2.6331211312104466,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,18,text,2,15,16,0.30000001192092896,0.3199999928474426,14,16,-0.09362240880727768,0.09362240880727768,opposes_predicted_class,predicted-class logit drop after local occlusion,– certified,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/18.mp4,5.642402624208928,6.018562425474644,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,18,audio,1,7,11,0.14000000059604645,0.2199999988079071,52,82,0.10665786266326904,0.10665786266326904,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 52–81; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/18.mp4,2.6331211312104466,4.137761737579107,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,18,audio,2,23,24,0.46000000834465027,0.47999998927116394,171,179,-0.01883687637746334,0.01883687637746334,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 171–178; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/18.mp4,8.65168383694625,9.027843638211966,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,18,vision,1,43,47,0.8600000143051147,0.9399999976158142,240,264,0.30359170585870743,0.30359170585870743,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 240–263; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/18.mp4,16.174887149050715,17.679527475158217,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_18_vision_1.jpg
+E2_MoFE_Shapley,18,vision,2,10,11,0.20000000298023224,0.2199999988079071,56,62,0.0711350217461586,0.0711350217461586,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 56–61; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/18.mp4,3.761601656052232,4.137761737579107,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_18_vision_2.jpg
+E1_MoFE_Router,18,text,1,38,39,0.7599999904632568,0.7799999713897705,38,39,0.633056640625,0.633056640625,router_activity_not_signed_contribution,internal router utility; not a prediction effect,fishery,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/18.mp4,14.29408590063286,14.670245701898576,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,18,text,2,7,8,0.14000000059604645,0.1599999964237213,7,8,0.6324462890625,0.6324462890625,router_activity_not_signed_contribution,internal router utility; not a prediction effect,first,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/18.mp4,2.6331211312104466,3.009281212737322,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,18,audio,1,38,39,0.7599999904632568,0.7799999713897705,282,291,0.51507568359375,0.51507568359375,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 282–290; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/18.mp4,14.29408590063286,14.670245701898576,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,18,audio,2,4,5,0.07999999821186066,0.10000000149011612,29,38,0.51483154296875,0.51483154296875,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 29–37; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/18.mp4,1.504640606368661,1.880800828026116,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,18,vision,1,10,12,0.20000000298023224,0.23999999463558197,56,68,1.234375,1.234375,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 56–67; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/18.mp4,3.761601656052232,4.513921819105983,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_18_vision_1.jpg
+E1_MoFE_Router,18,vision,2,2,3,0.03999999910593033,0.05999999865889549,11,17,0.61798095703125,0.61798095703125,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 11–16; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/18.mp4,0.7523203031843305,1.1284804547764957,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_18_vision_2.jpg
+E0_EarlyConcat,19,text,1,35,37,0.699999988079071,0.7400000095367432,28,31,-0.22915848344564438,0.22915848344564438,opposes_predicted_class,predicted-class logit drop after local occlusion,over imperfections,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/19.mp4,9.57809983688593,10.125420130491257,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,19,text,2,40,42,0.800000011920929,0.8399999737739563,32,35,0.22308466583490372,0.22308466583490372,supports_predicted_class,predicted-class logit drop after local occlusion,but for the,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/19.mp4,10.94640016311407,11.493719641149044,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,19,audio,1,16,19,0.3199999928474426,0.3799999952316284,87,104,-0.09844603389501572,0.09844603389501572,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 87–103; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/19.mp4,4.378559902131557,5.199539934754371,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,19,audio,2,0,2,0.0,0.03999999910593033,0,11,-0.06767807342112064,0.06767807342112064,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 0–10; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/19.mp4,0.0,0.5473199877664446,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,19,vision,1,31,34,0.6200000047683716,0.6800000071525574,126,139,0.16705993935465813,0.16705993935465813,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 126–138; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/19.mp4,8.483460065245628,9.304440097868442,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_19_vision_1.jpg
+E0_EarlyConcat,19,vision,2,27,29,0.5400000214576721,0.5799999833106995,110,119,0.10459240153431892,0.10459240153431892,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 110–118; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/19.mp4,7.3888202936053275,7.936139771640301,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_19_vision_2.jpg
+E2_MoFE_Shapley,19,text,1,34,37,0.6800000071525574,0.7400000095367432,27,31,-0.4073387011885643,0.4073387011885643,opposes_predicted_class,predicted-class logit drop after local occlusion,all over imperfections,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/19.mp4,9.304440097868442,10.125420130491257,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,19,text,2,12,14,0.23999999463558197,0.2800000011920929,9,12,-0.2417151853442192,0.2417151853442192,opposes_predicted_class,predicted-class logit drop after local occlusion,own up to,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/19.mp4,3.283919926598668,3.831240016311407,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,19,audio,1,16,19,0.3199999928474426,0.3799999952316284,87,104,-0.10513635538518429,0.10513635538518429,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 87–103; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/19.mp4,4.378559902131557,5.199539934754371,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,19,audio,2,0,2,0.0,0.03999999910593033,0,11,-0.09250686690211296,0.09250686690211296,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 0–10; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/19.mp4,0.0,0.5473199877664446,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,19,vision,1,26,31,0.5199999809265137,0.6200000047683716,106,127,0.47280463576316833,0.47280463576316833,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 106–126; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/19.mp4,7.1151597390174866,8.483460065245628,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_19_vision_1.jpg
+E1_MoFE_Router,19,text,1,45,47,0.8999999761581421,0.9399999976158142,36,39,1.264404296875,1.264404296875,router_activity_not_signed_contribution,internal router utility; not a prediction effect,"part, people",bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/19.mp4,12.314699673771859,12.862019967377186,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,19,text,2,30,31,0.6000000238418579,0.6200000047683716,24,26,0.6319580078125,0.6319580078125,router_activity_not_signed_contribution,internal router utility; not a prediction effect,fingers and,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/19.mp4,8.209800326228141,8.483460065245628,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,19,audio,1,45,47,0.8999999761581421,0.9399999976158142,244,256,1.02752685546875,1.02752685546875,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 244–255; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/19.mp4,12.314699673771859,12.862019967377186,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,19,audio,2,30,32,0.6000000238418579,0.6399999856948853,163,175,1.02685546875,1.02685546875,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 163–174; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/19.mp4,8.209800326228141,8.757119804263114,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,19,vision,1,7,10,0.14000000059604645,0.20000000298023224,28,41,1.85888671875,1.85888671875,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 28–40; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/19.mp4,1.9156200081557035,2.7366000407785176,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_19_vision_1.jpg
+E1_MoFE_Router,19,vision,2,20,21,0.4000000059604645,0.41999998688697815,81,86,0.62017822265625,0.62017822265625,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned vision feature rows 81–85; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/19.mp4,5.473200081557035,5.746859820574522,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E1_MoFE_Router_19_vision_2.jpg
+E0_EarlyConcat,20,text,1,24,27,0.47999998927116394,0.5400000214576721,21,25,0.3322090730071068,0.3322090730071068,supports_predicted_class,predicted-class logit drop after local occlusion,ll have more live,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/20.mp4,5.257437482487112,5.9146175350257755,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,20,text,2,11,13,0.2199999988079071,0.25999999046325684,9,12,0.2152409553527832,0.2152409553527832,supports_predicted_class,predicted-class logit drop after local occlusion,of the description,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/20.mp4,2.4096588869430122,2.8477785955440997,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,20,audio,1,33,35,0.6600000262260437,0.699999988079071,142,152,-0.016775568015873432,0.016775568015873432,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 142–151; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/20.mp4,7.228976987253725,7.667096369430125,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,20,audio,2,41,43,0.8199999928474426,0.8600000143051147,177,186,-0.016016324516385794,0.016016324516385794,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 177–185; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/20.mp4,8.981455821658075,9.41957585668385,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E0_EarlyConcat,20,vision,1,42,45,0.8399999737739563,0.8999999761581421,136,146,0.0797722339630127,0.0797722339630127,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 136–145; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/20.mp4,9.200515512746275,9.857695238860249,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_20_vision_1.jpg
+E0_EarlyConcat,20,vision,2,15,17,0.30000001192092896,0.3400000035762787,48,56,-0.04813774488866329,0.04813774488866329,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 48–55; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/20.mp4,3.285898630569875,3.7240183391709625,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E0_EarlyConcat_20_vision_2.jpg
+E2_MoFE_Shapley,20,text,1,11,14,0.2199999988079071,0.2800000011920929,9,13,0.3986911475658417,0.3986911475658417,supports_predicted_class,predicted-class logit drop after local occlusion,of the description of,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/20.mp4,2.4096588869430122,3.0668386130569876,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,20,text,2,28,29,0.5600000023841858,0.5799999833106995,25,27,0.1323462277650833,0.1323462277650833,supports_predicted_class,predicted-class logit drop after local occlusion,updates and,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/20.mp4,6.133677226113975,6.352736917202175,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,20,audio,1,33,35,0.6600000262260437,0.699999988079071,142,152,-0.040185969322919846,0.040185969322919846,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 142–151; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/20.mp4,7.228976987253725,7.667096369430125,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,20,audio,2,26,28,0.5199999809265137,0.5600000023841858,112,121,0.028074702247977257,0.028074702247977257,supports_predicted_class,predicted-class logit drop after local occlusion,unaligned audio feature rows 112–120; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/20.mp4,5.6955571910881995,6.133677226113975,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E2_MoFE_Shapley,20,vision,1,13,16,0.25999999046325684,0.3199999928474426,42,52,-0.12262038514018059,0.12262038514018059,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 42–51; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/20.mp4,2.8477785955440997,3.5049583216580746,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_20_vision_1.jpg
+E2_MoFE_Shapley,20,vision,2,34,36,0.6800000071525574,0.7200000286102295,110,117,-0.08428136259317398,0.08428136259317398,opposes_predicted_class,predicted-class logit drop after local occlusion,unaligned vision feature rows 110–116; candidate frame time is estimated from relative progress,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/20.mp4,7.448036678341925,7.886156713367701,"relative progress times clip duration; approximate, not a physical feature timestamp",evidence_frames/E2_MoFE_Shapley_20_vision_2.jpg
+E1_MoFE_Router,20,text,1,15,17,0.30000001192092896,0.3400000035762787,13,16,1.266845703125,1.266845703125,router_activity_not_signed_contribution,internal router utility; not a prediction effect,this video for,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/20.mp4,3.285898630569875,3.7240183391709625,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,20,text,2,33,35,0.6600000262260437,0.699999988079071,29,32,1.26611328125,1.26611328125,router_activity_not_signed_contribution,internal router utility; not a prediction effect,market video (,bert_token_ids,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/20.mp4,7.228976987253725,7.667096369430125,"relative progress times clip duration; approximate, not a physical feature timestamp",
+E1_MoFE_Router,20,audio,1,33,36,0.6600000262260437,0.7200000286102295,142,156,1.54534912109375,1.54534912109375,router_activity_not_signed_contribution,internal router utility; not a prediction effect,unaligned audio feature rows 142–155; review the linked source clip at the estimated relative span,feature_row_provenance,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/20.mp4,7.228976987253725,7.886156713367701,"relative progress times clip duration; approximate, not a physical feature timestamp",
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diff --git a/final/output/q3/first_round/attachment4_local_evidence.csv b/final/output/q3/first_round/attachment4_local_evidence.csv
new file mode 100644
index 0000000..a1ea4f7
--- /dev/null
+++ b/final/output/q3/first_round/attachment4_local_evidence.csv
@@ -0,0 +1,5991 @@
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diff --git a/final/output/q3/first_round/attachment4_modal_shapley.csv b/final/output/q3/first_round/attachment4_modal_shapley.csv
new file mode 100644
index 0000000..537fe36
--- /dev/null
+++ b/final/output/q3/first_round/attachment4_modal_shapley.csv
@@ -0,0 +1,41 @@
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diff --git a/final/output/q3/first_round/attachment4_pairwise_interactions.csv b/final/output/q3/first_round/attachment4_pairwise_interactions.csv
new file mode 100644
index 0000000..a16cbea
--- /dev/null
+++ b/final/output/q3/first_round/attachment4_pairwise_interactions.csv
@@ -0,0 +1,41 @@
+base_model,case_id,class_interaction_TA,regression_interaction_TA,class_interaction_TV,regression_interaction_TV,class_interaction_AV,regression_interaction_AV
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diff --git a/final/output/q3/first_round/attachment4_predictions.csv b/final/output/q3/first_round/attachment4_predictions.csv
new file mode 100644
index 0000000..32e2af9
--- /dev/null
+++ b/final/output/q3/first_round/attachment4_predictions.csv
@@ -0,0 +1,61 @@
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+E1_MoFE_Router,mofe,01,1,neutral,0.25077587366104126,0.47660401463508606,0.1514279842376709,0.47660401463508606,0.37196803092956543,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/01.mp4,router profile; not itself a counterfactual contribution
+E2_MoFE_Shapley,mofe,01,1,neutral,0.25077587366104126,0.47660401463508606,0.1514279842376709,0.47660401463508606,0.37196803092956543,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/01.mp4,exact Shapley + multiscale occlusion
+E0_EarlyConcat,early_concat,02,2,positive,0.394508421421051,0.6335527300834656,0.09172090888023376,0.27472639083862305,0.6335527300834656,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/02.mp4,exact Shapley + multiscale occlusion
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+E2_MoFE_Shapley,mofe,02,1,neutral,0.11008816212415695,0.4620336592197418,0.14684437215328217,0.4620336592197418,0.3911219835281372,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/02.mp4,exact Shapley + multiscale occlusion
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+E1_MoFE_Router,mofe,03,0,negative,-0.61025071144104,0.6876164078712463,0.6876164078712463,0.17326515913009644,0.13911846280097961,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/03.mp4,router profile; not itself a counterfactual contribution
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+E0_EarlyConcat,early_concat,08,2,positive,0.8362821936607361,0.7786725759506226,0.018857697024941444,0.20246975123882294,0.7786725759506226,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/08.mp4,exact Shapley + multiscale occlusion
+E1_MoFE_Router,mofe,08,2,positive,0.7118145227432251,0.8124023079872131,0.014303640462458134,0.1732940673828125,0.8124023079872131,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/08.mp4,router profile; not itself a counterfactual contribution
+E2_MoFE_Shapley,mofe,08,2,positive,0.7118145227432251,0.8124023079872131,0.014303640462458134,0.1732940673828125,0.8124023079872131,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/08.mp4,exact Shapley + multiscale occlusion
+E0_EarlyConcat,early_concat,09,0,negative,-1.7497034072875977,0.9198840856552124,0.9198840856552124,0.04435630142688751,0.03575955703854561,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/09.mp4,exact Shapley + multiscale occlusion
+E1_MoFE_Router,mofe,09,0,negative,-1.8478950262069702,0.9177744388580322,0.9177744388580322,0.04749597981572151,0.03472955524921417,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/09.mp4,router profile; not itself a counterfactual contribution
+E2_MoFE_Shapley,mofe,09,0,negative,-1.8478950262069702,0.9177744388580322,0.9177744388580322,0.04749597981572151,0.03472955524921417,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/09.mp4,exact Shapley + multiscale occlusion
+E0_EarlyConcat,early_concat,10,0,negative,-0.9537990093231201,0.7828088402748108,0.7828088402748108,0.057208795100450516,0.15998226404190063,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/10.mp4,exact Shapley + multiscale occlusion
+E1_MoFE_Router,mofe,10,0,negative,-1.2599782943725586,0.861793577671051,0.861793577671051,0.0671548917889595,0.07105153799057007,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/10.mp4,router profile; not itself a counterfactual contribution
+E2_MoFE_Shapley,mofe,10,0,negative,-1.2599782943725586,0.861793577671051,0.861793577671051,0.0671548917889595,0.07105153799057007,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/10.mp4,exact Shapley + multiscale occlusion
+E0_EarlyConcat,early_concat,11,0,negative,-0.4693428874015808,0.6141843199729919,0.6141843199729919,0.15715409815311432,0.22866155207157135,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/11.mp4,exact Shapley + multiscale occlusion
+E1_MoFE_Router,mofe,11,0,negative,-0.4920114278793335,0.5773057341575623,0.5773057341575623,0.2606673538684845,0.16202691197395325,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/11.mp4,router profile; not itself a counterfactual contribution
+E2_MoFE_Shapley,mofe,11,0,negative,-0.4920114278793335,0.5773057341575623,0.5773057341575623,0.2606673538684845,0.16202691197395325,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/11.mp4,exact Shapley + multiscale occlusion
+E0_EarlyConcat,early_concat,12,0,negative,-1.2375768423080444,0.8481571078300476,0.8481571078300476,0.11418862640857697,0.037654224783182144,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/12.mp4,exact Shapley + multiscale occlusion
+E1_MoFE_Router,mofe,12,0,negative,-1.088169813156128,0.8138288855552673,0.8138288855552673,0.11932305246591568,0.06684806942939758,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/12.mp4,router profile; not itself a counterfactual contribution
+E2_MoFE_Shapley,mofe,12,0,negative,-1.088169813156128,0.8138288855552673,0.8138288855552673,0.11932305246591568,0.06684806942939758,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/12.mp4,exact Shapley + multiscale occlusion
+E0_EarlyConcat,early_concat,13,1,neutral,-0.05632396042346954,0.6167923212051392,0.05960126221179962,0.6167923212051392,0.3236064314842224,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/13.mp4,exact Shapley + multiscale occlusion
+E1_MoFE_Router,mofe,13,2,positive,0.42077967524528503,0.5249780416488647,0.044542502611875534,0.4304795265197754,0.5249780416488647,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/13.mp4,router profile; not itself a counterfactual contribution
+E2_MoFE_Shapley,mofe,13,2,positive,0.42077967524528503,0.5249780416488647,0.044542502611875534,0.4304795265197754,0.5249780416488647,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/13.mp4,exact Shapley + multiscale occlusion
+E0_EarlyConcat,early_concat,14,2,positive,0.7671283483505249,0.6980242133140564,0.04740103706717491,0.2545747756958008,0.6980242133140564,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/14.mp4,exact Shapley + multiscale occlusion
+E1_MoFE_Router,mofe,14,2,positive,0.526305079460144,0.5815858244895935,0.0552791990339756,0.3631349205970764,0.5815858244895935,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/14.mp4,router profile; not itself a counterfactual contribution
+E2_MoFE_Shapley,mofe,14,2,positive,0.526305079460144,0.5815858244895935,0.0552791990339756,0.3631349205970764,0.5815858244895935,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/14.mp4,exact Shapley + multiscale occlusion
+E0_EarlyConcat,early_concat,15,2,positive,1.7079981565475464,0.9449816942214966,0.009686050936579704,0.04533228650689125,0.9449816942214966,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/15.mp4,exact Shapley + multiscale occlusion
+E1_MoFE_Router,mofe,15,2,positive,1.0526602268218994,0.8927561044692993,0.00900923740118742,0.09823452681303024,0.8927561044692993,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/15.mp4,router profile; not itself a counterfactual contribution
+E2_MoFE_Shapley,mofe,15,2,positive,1.0526602268218994,0.8927561044692993,0.00900923740118742,0.09823452681303024,0.8927561044692993,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/15.mp4,exact Shapley + multiscale occlusion
+E0_EarlyConcat,early_concat,16,0,negative,-1.7967978715896606,0.943943202495575,0.943943202495575,0.03545090928673744,0.02060585841536522,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/16.mp4,exact Shapley + multiscale occlusion
+E1_MoFE_Router,mofe,16,0,negative,-1.835618019104004,0.9136225581169128,0.9136225581169128,0.05045180395245552,0.03592574596405029,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/16.mp4,router profile; not itself a counterfactual contribution
+E2_MoFE_Shapley,mofe,16,0,negative,-1.835618019104004,0.9136225581169128,0.9136225581169128,0.05045180395245552,0.03592574596405029,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/16.mp4,exact Shapley + multiscale occlusion
+E0_EarlyConcat,early_concat,17,2,positive,1.7792155742645264,0.9510148167610168,0.008391551673412323,0.040593668818473816,0.9510148167610168,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/17.mp4,exact Shapley + multiscale occlusion
+E1_MoFE_Router,mofe,17,2,positive,0.9275866746902466,0.8838974833488464,0.008321800269186497,0.10778070986270905,0.8838974833488464,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/17.mp4,router profile; not itself a counterfactual contribution
+E2_MoFE_Shapley,mofe,17,2,positive,0.9275866746902466,0.8838974833488464,0.008321800269186497,0.10778070986270905,0.8838974833488464,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/17.mp4,exact Shapley + multiscale occlusion
+E0_EarlyConcat,early_concat,18,1,neutral,-0.3791736364364624,0.5066172480583191,0.3373114764690399,0.5066172480583191,0.15607130527496338,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/18.mp4,exact Shapley + multiscale occlusion
+E1_MoFE_Router,mofe,18,0,negative,-0.17120634019374847,0.49046963453292847,0.49046963453292847,0.3366304337978363,0.17289996147155762,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/18.mp4,router profile; not itself a counterfactual contribution
+E2_MoFE_Shapley,mofe,18,0,negative,-0.17120634019374847,0.49046963453292847,0.49046963453292847,0.3366304337978363,0.17289996147155762,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/18.mp4,exact Shapley + multiscale occlusion
+E0_EarlyConcat,early_concat,19,2,positive,0.029986131936311722,0.49594828486442566,0.42628589272499084,0.0777658224105835,0.49594828486442566,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/19.mp4,exact Shapley + multiscale occlusion
+E1_MoFE_Router,mofe,19,2,positive,0.04691345617175102,0.47389325499534607,0.34708499908447266,0.17902177572250366,0.47389325499534607,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/19.mp4,router profile; not itself a counterfactual contribution
+E2_MoFE_Shapley,mofe,19,2,positive,0.04691345617175102,0.47389325499534607,0.34708499908447266,0.17902177572250366,0.47389325499534607,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/19.mp4,exact Shapley + multiscale occlusion
+E0_EarlyConcat,early_concat,20,2,positive,1.2690019607543945,0.8650258183479309,0.0169941745698452,0.1179799884557724,0.8650258183479309,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/20.mp4,exact Shapley + multiscale occlusion
+E1_MoFE_Router,mofe,20,2,positive,0.7595000267028809,0.8352178931236267,0.01265318039804697,0.15212883055210114,0.8352178931236267,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/20.mp4,router profile; not itself a counterfactual contribution
+E2_MoFE_Shapley,mofe,20,2,positive,0.7595000267028809,0.8352178931236267,0.01265318039804697,0.15212883055210114,0.8352178931236267,附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/20.mp4,exact Shapley + multiscale occlusion
diff --git a/final/output/q3/first_round/attachment4_router_local_evidence.csv b/final/output/q3/first_round/attachment4_router_local_evidence.csv
new file mode 100644
index 0000000..add24a0
--- /dev/null
+++ b/final/output/q3/first_round/attachment4_router_local_evidence.csv
@@ -0,0 +1,2996 @@
+case_id,modality,slot_0based,router_utility,relative_progress_start,relative_progress_end
+01,text,0,0.640625,0.0,0.019999999552965164
+01,text,1,0.6402587890625,0.019999999552965164,0.03999999910593033
+01,text,2,0.6395263671875,0.03999999910593033,0.05999999865889549
+01,text,3,0.6395263671875,0.05999999865889549,0.07999999821186066
+01,text,4,0.6409912109375,0.07999999821186066,0.10000000149011612
+01,text,5,0.640625,0.10000000149011612,0.11999999731779099
+01,text,6,0.6400146484375,0.11999999731779099,0.14000000059604645
+01,text,7,0.6402587890625,0.14000000059604645,0.1599999964237213
+01,text,8,0.6397705078125,0.1599999964237213,0.18000000715255737
+01,text,9,0.640380859375,0.18000000715255737,0.20000000298023224
+01,text,10,0.64013671875,0.20000000298023224,0.2199999988079071
+01,text,11,0.640380859375,0.2199999988079071,0.23999999463558197
+01,text,12,0.640869140625,0.23999999463558197,0.25999999046325684
+01,text,13,0.6405029296875,0.25999999046325684,0.2800000011920929
+01,text,14,0.640625,0.2800000011920929,0.30000001192092896
+01,text,15,0.640380859375,0.30000001192092896,0.3199999928474426
+01,text,16,0.6407470703125,0.3199999928474426,0.3400000035762787
+01,text,17,0.640625,0.3400000035762787,0.36000001430511475
+01,text,18,0.640625,0.36000001430511475,0.3799999952316284
+01,text,19,0.6402587890625,0.3799999952316284,0.4000000059604645
+01,text,20,0.640625,0.4000000059604645,0.41999998688697815
+01,text,21,0.640380859375,0.41999998688697815,0.4399999976158142
+01,text,22,0.640380859375,0.4399999976158142,0.46000000834465027
+01,text,23,0.6396484375,0.46000000834465027,0.47999998927116394
+01,text,24,0.6396484375,0.47999998927116394,0.5
+01,text,25,0.6396484375,0.5,0.5199999809265137
+01,text,26,0.6396484375,0.5199999809265137,0.5400000214576721
+01,text,27,0.6405029296875,0.5400000214576721,0.5600000023841858
+01,text,28,0.640625,0.5600000023841858,0.5799999833106995
+01,text,29,0.640625,0.5799999833106995,0.6000000238418579
+01,text,30,0.640380859375,0.6000000238418579,0.6200000047683716
+01,text,31,0.6409912109375,0.6200000047683716,0.6399999856948853
+01,text,32,0.6405029296875,0.6399999856948853,0.6600000262260437
+01,text,33,0.640869140625,0.6600000262260437,0.6800000071525574
+01,text,34,0.6402587890625,0.6800000071525574,0.699999988079071
+01,text,35,0.6409912109375,0.699999988079071,0.7200000286102295
+01,text,36,0.6409912109375,0.7200000286102295,0.7400000095367432
+01,text,37,0.640380859375,0.7400000095367432,0.7599999904632568
+01,text,38,0.6402587890625,0.7599999904632568,0.7799999713897705
+01,text,39,0.6405029296875,0.7799999713897705,0.800000011920929
+01,text,40,0.6405029296875,0.800000011920929,0.8199999928474426
+01,text,41,0.6407470703125,0.8199999928474426,0.8399999737739563
+01,text,42,0.6395263671875,0.8399999737739563,0.8600000143051147
+01,text,43,0.64013671875,0.8600000143051147,0.8799999952316284
+01,text,44,0.640625,0.8799999952316284,0.8999999761581421
+01,text,45,0.6402587890625,0.8999999761581421,0.9200000166893005
+01,text,46,0.639892578125,0.9200000166893005,0.9399999976158142
+01,text,47,0.639892578125,0.9399999976158142,0.9599999785423279
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+01,audio,0,0.5198974609375,0.0,0.019999999552965164
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+# Q3 explanation card — E0_EarlyConcat — 01
+
+## Prediction
+
+- Polarity: **neutral**
+- Intensity: -0.172
+- Predicted-class confidence: 0.565
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/01.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +1.0003 | 0.819 | -0.4419 | 0.510 |
+| audio | -0.1551 | 0.127 | +0.1493 | 0.172 |
+| vision | -0.0656 | 0.054 | -0.2749 | 0.317 |
+
+Shapley completeness residuals: class 0.00e+00, intensity 0.00e+00.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | -0.0683 | -0.0865 |
+| Text + Vision | -0.0051 | +0.3554 |
+| Audio + Vision | +0.0441 | +0.1078 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 19–21 (relative progress 0.380–0.440), estimated clip interval 3.42–3.96s** — supports_predicted_class; evidence: belt is
+- **text bins 47–47 (relative progress 0.940–0.960), estimated clip interval 8.46–8.64s** — supports_predicted_class; evidence: requirements
+- **audio bins 22–24 (relative progress 0.440–0.500), estimated clip interval 3.96–4.50s** — opposes_predicted_class; evidence: unaligned audio feature rows 78–89; review the linked source clip at the estimated relative span
+- **audio bins 3–4 (relative progress 0.060–0.100), estimated clip interval 0.54–0.90s** — opposes_predicted_class; evidence: unaligned audio feature rows 10–17; review the linked source clip at the estimated relative span
+- **vision bins 46–48 (relative progress 0.920–0.980), estimated clip interval 8.28–8.82s** — opposes_predicted_class; evidence: unaligned vision feature rows 124–132; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_01_vision_1.jpg)
+- **vision bins 32–33 (relative progress 0.640–0.680), estimated clip interval 5.76–6.12s** — opposes_predicted_class; evidence: unaligned vision feature rows 86–91; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_01_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E0_EarlyConcat/01.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.3528 / +0.9214 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: -0.1364 / -0.3016. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +0.7019.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+Replacing these wear components when replacing the timing belt is essential to ensuring the new belt performs to its mileage requirements
diff --git a/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/02.md b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/02.md
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index 0000000..1d63897
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/02.md
@@ -0,0 +1,56 @@
+# Q3 explanation card — E0_EarlyConcat — 02
+
+## Prediction
+
+- Polarity: **positive**
+- Intensity: +0.395
+- Predicted-class confidence: 0.634
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/02.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +0.6519 | 0.573 | +0.1487 | 0.332 |
+| audio | +0.1355 | 0.119 | +0.0748 | 0.167 |
+| vision | -0.3508 | 0.308 | -0.2239 | 0.500 |
+
+Shapley completeness residuals: class 5.55e-17, intensity -2.78e-17.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | -0.0716 | +0.0491 |
+| Text + Vision | +0.1147 | +0.0187 |
+| Audio + Vision | +0.0890 | +0.0524 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 22–26 (relative progress 0.440–0.540), estimated clip interval 1.54–1.89s** — supports_predicted_class; evidence: s happiness -
+- **audio bins 35–39 (relative progress 0.700–0.800), estimated clip interval 2.45–2.79s** — supports_predicted_class; evidence: unaligned audio feature rows 46–52; review the linked source clip at the estimated relative span
+- **vision bins 10–12 (relative progress 0.200–0.260), estimated clip interval 0.70–0.91s** — opposes_predicted_class; evidence: unaligned vision feature rows 10–12; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_02_vision_1.jpg)
+- **vision bins 34–34 (relative progress 0.680–0.700), estimated clip interval 2.38–2.45s** — opposes_predicted_class; evidence: unaligned vision feature rows 34–35; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_02_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E0_EarlyConcat/02.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.4557 / +0.4939 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: -1.1868 / -0.5516. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +0.4518.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+We want to live by each other’s happiness - not by each other’s misery.
diff --git a/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/03.md b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/03.md
new file mode 100644
index 0000000..55835f3
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/03.md
@@ -0,0 +1,57 @@
+# Q3 explanation card — E0_EarlyConcat — 03
+
+## Prediction
+
+- Polarity: **negative**
+- Intensity: -0.493
+- Predicted-class confidence: 0.553
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/03.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +1.1502 | 0.628 | -1.0738 | 0.671 |
+| audio | +0.1589 | 0.087 | -0.1704 | 0.106 |
+| vision | -0.5213 | 0.285 | +0.3565 | 0.223 |
+
+Shapley completeness residuals: class -1.11e-16, intensity 2.22e-16.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | -0.1032 | +0.2050 |
+| Text + Vision | +0.3268 | -0.2207 |
+| Audio + Vision | +0.0454 | +0.0051 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 30–34 (relative progress 0.600–0.700), estimated clip interval 5.80–6.76s** — supports_predicted_class; evidence: take rental income into
+- **audio bins 14–17 (relative progress 0.280–0.360), estimated clip interval 2.71–3.48s** — supports_predicted_class; evidence: unaligned audio feature rows 53–68; review the linked source clip at the estimated relative span
+- **audio bins 48–48 (relative progress 0.960–0.980), estimated clip interval 9.28–9.47s** — supports_predicted_class; evidence: unaligned audio feature rows 183–187; review the linked source clip at the estimated relative span
+- **vision bins 17–19 (relative progress 0.340–0.400), estimated clip interval 3.29–3.87s** — opposes_predicted_class; evidence: unaligned vision feature rows 48–57; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_03_vision_1.jpg)
+- **vision bins 2–2 (relative progress 0.040–0.060), estimated clip interval 0.39–0.58s** — opposes_predicted_class; evidence: unaligned vision feature rows 5–8; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_03_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E0_EarlyConcat/03.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.6246 / +1.3268 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: -1.1954 / -0.3649. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +1.0492.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+There's one lender at the moment which I think is just Bankwest who don't take rental income into account, they take rental yield into account.
diff --git a/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/04.md b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/04.md
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+# Q3 explanation card — E0_EarlyConcat — 04
+
+## Prediction
+
+- Polarity: **negative**
+- Intensity: -0.968
+- Predicted-class confidence: 0.769
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/04.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +1.6860 | 0.849 | -1.7115 | 0.831 |
+| audio | -0.2440 | 0.123 | +0.2778 | 0.135 |
+| vision | +0.0555 | 0.028 | +0.0707 | 0.034 |
+
+Shapley completeness residuals: class -4.44e-16, intensity 4.44e-16.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | +0.1306 | -0.0822 |
+| Text + Vision | -0.0863 | +0.0593 |
+| Audio + Vision | -0.0342 | +0.0247 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 5–6 (relative progress 0.100–0.140), estimated clip interval 0.70–0.98s** — supports_predicted_class; evidence: i blow
+- **text bins 44–45 (relative progress 0.880–0.920), estimated clip interval 6.15–6.43s** — supports_predicted_class; evidence: absolutely not
+- **audio bins 11–14 (relative progress 0.220–0.300), estimated clip interval 1.54–2.10s** — opposes_predicted_class; evidence: unaligned audio feature rows 30–41; review the linked source clip at the estimated relative span
+- **audio bins 37–37 (relative progress 0.740–0.760), estimated clip interval 5.18–5.32s** — opposes_predicted_class; evidence: unaligned audio feature rows 101–104; review the linked source clip at the estimated relative span
+- **vision bins 35–39 (relative progress 0.700–0.800), estimated clip interval 4.90–5.60s** — opposes_predicted_class; evidence: unaligned vision feature rows 72–82; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_04_vision_1.jpg)
+
+![Local evidence profile](../../evidence_profiles/E0_EarlyConcat/04.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.9337 / +1.7442 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: -0.6584 / -0.3517. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +1.4210.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+If I blow it at the team exercise, should I kiss my chances of cheering "GO BLUE" goodbye?] Absolutely not.
diff --git a/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/05.md b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/05.md
new file mode 100644
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+# Q3 explanation card — E0_EarlyConcat — 05
+
+## Prediction
+
+- Polarity: **positive**
+- Intensity: +1.129
+- Predicted-class confidence: 0.848
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/05.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +0.4992 | 0.376 | +0.0947 | 0.129 |
+| audio | +0.1165 | 0.088 | +0.0709 | 0.097 |
+| vision | +0.7112 | 0.536 | +0.5687 | 0.774 |
+
+Shapley completeness residuals: class 0.00e+00, intensity 0.00e+00.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | +0.1349 | +0.1991 |
+| Text + Vision | -0.1658 | +0.1123 |
+| Audio + Vision | -0.0532 | -0.0378 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 0–2 (relative progress 0.000–0.060), estimated clip interval 0.00–0.37s** — supports_predicted_class; evidence: hi
+- **text bins 6–7 (relative progress 0.120–0.160), estimated clip interval 0.73–0.98s** — supports_predicted_class; evidence: ,
+- **audio bins 18–20 (relative progress 0.360–0.420), estimated clip interval 2.20–2.56s** — supports_predicted_class; evidence: unaligned audio feature rows 43–50; review the linked source clip at the estimated relative span
+- **audio bins 40–41 (relative progress 0.800–0.840), estimated clip interval 4.88–5.12s** — supports_predicted_class; evidence: unaligned audio feature rows 96–100; review the linked source clip at the estimated relative span
+- **vision bins 21–24 (relative progress 0.420–0.500), estimated clip interval 2.56–3.05s** — supports_predicted_class; evidence: unaligned vision feature rows 37–44; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_05_vision_1.jpg)
+- **vision bins 44–44 (relative progress 0.880–0.900), estimated clip interval 5.37–5.49s** — supports_predicted_class; evidence: unaligned vision feature rows 79–80; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_05_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E0_EarlyConcat/05.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.2997 / +0.7538 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.0442 / +0.0098. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +0.7515.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+Hi, my name is Chloe, video marketer for Red Wagon Marketing.
diff --git a/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/06.md b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/06.md
new file mode 100644
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--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/06.md
@@ -0,0 +1,58 @@
+# Q3 explanation card — E0_EarlyConcat — 06
+
+## Prediction
+
+- Polarity: **positive**
+- Intensity: +1.201
+- Predicted-class confidence: 0.849
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/06.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +1.7299 | 0.798 | +1.0189 | 0.827 |
+| audio | -0.1445 | 0.067 | -0.2116 | 0.172 |
+| vision | -0.2938 | 0.135 | -0.0010 | 0.001 |
+
+Shapley completeness residuals: class 0.00e+00, intensity 0.00e+00.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | -0.1320 | -0.0408 |
+| Text + Vision | +0.0014 | -0.1078 |
+| Audio + Vision | +0.1490 | +0.0687 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 8–10 (relative progress 0.160–0.220), estimated clip interval 1.33–1.83s** — supports_predicted_class; evidence: a fan of dancing
+- **text bins 21–22 (relative progress 0.420–0.460), estimated clip interval 3.49–3.82s** — supports_predicted_class; evidence: to watch people
+- **audio bins 5–8 (relative progress 0.100–0.180), estimated clip interval 0.83–1.49s** — opposes_predicted_class; evidence: unaligned audio feature rows 16–29; review the linked source clip at the estimated relative span
+- **audio bins 38–38 (relative progress 0.760–0.780), estimated clip interval 6.31–6.48s** — opposes_predicted_class; evidence: unaligned audio feature rows 123–127; review the linked source clip at the estimated relative span
+- **vision bins 33–36 (relative progress 0.660–0.740), estimated clip interval 5.48–6.15s** — opposes_predicted_class; evidence: unaligned vision feature rows 81–91; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_06_vision_1.jpg)
+- **vision bins 40–40 (relative progress 0.800–0.820), estimated clip interval 6.64–6.81s** — opposes_predicted_class; evidence: unaligned vision feature rows 98–100; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_06_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E0_EarlyConcat/06.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.5652 / +1.4485 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: -0.4569 / -0.5398. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +1.1669.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+If you're a fan of dancing in that sense, just like to watch people dance, see impressive dance moves then you might want to check out this movie solely for that
diff --git a/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/07.md b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/07.md
new file mode 100644
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--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/07.md
@@ -0,0 +1,57 @@
+# Q3 explanation card — E0_EarlyConcat — 07
+
+## Prediction
+
+- Polarity: **positive**
+- Intensity: +0.852
+- Predicted-class confidence: 0.808
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/07.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +1.2071 | 0.949 | +0.7027 | 0.741 |
+| audio | +0.0179 | 0.014 | -0.0444 | 0.047 |
+| vision | -0.0466 | 0.037 | -0.2015 | 0.212 |
+
+Shapley completeness residuals: class -2.22e-16, intensity -1.11e-16.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | +0.0059 | +0.1040 |
+| Text + Vision | +0.1672 | +0.1852 |
+| Audio + Vision | +0.0742 | +0.1056 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 23–27 (relative progress 0.460–0.560), estimated clip interval 9.82–11.95s** — supports_predicted_class; evidence: the topics of growing the
+- **audio bins 1–3 (relative progress 0.020–0.080), estimated clip interval 0.43–1.71s** — supports_predicted_class; evidence: unaligned audio feature rows 8–33; review the linked source clip at the estimated relative span
+- **audio bins 11–12 (relative progress 0.220–0.260), estimated clip interval 4.70–5.55s** — supports_predicted_class; evidence: unaligned audio feature rows 93–110; review the linked source clip at the estimated relative span
+- **vision bins 10–13 (relative progress 0.200–0.280), estimated clip interval 4.27–5.98s** — supports_predicted_class; evidence: unaligned vision feature rows 63–89; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_07_vision_1.jpg)
+- **vision bins 4–4 (relative progress 0.080–0.100), estimated clip interval 1.71–2.13s** — supports_predicted_class; evidence: unaligned vision feature rows 25–31; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_07_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E0_EarlyConcat/07.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.5147 / +1.1770 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: -0.5447 / -0.0666. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +1.0335.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+As Linn’s associate editor Michael Baadke reports in our November 28 issue, attendees “enthusiastically discussed the topics of growing the hobby, the future of stamp shows, and dealers and philatelic partnerships, along with ways the leading organizations involved in the stamp hobby can work together to make it succeed and grow
diff --git a/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/08.md b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/08.md
new file mode 100644
index 0000000..ea34243
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/08.md
@@ -0,0 +1,58 @@
+# Q3 explanation card — E0_EarlyConcat — 08
+
+## Prediction
+
+- Polarity: **positive**
+- Intensity: +0.836
+- Predicted-class confidence: 0.779
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/08.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +1.6981 | 0.750 | +0.7934 | 0.693 |
+| audio | -0.0794 | 0.035 | -0.0434 | 0.038 |
+| vision | -0.4875 | 0.215 | -0.3086 | 0.269 |
+
+Shapley completeness residuals: class 0.00e+00, intensity 0.00e+00.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | -0.0518 | -0.0102 |
+| Text + Vision | +0.2869 | +0.1244 |
+| Audio + Vision | +0.1742 | +0.1245 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 38–40 (relative progress 0.760–0.820), estimated clip interval 3.87–4.17s** — supports_predicted_class; evidence: design grand
+- **text bins 20–20 (relative progress 0.400–0.420), estimated clip interval 2.03–2.14s** — supports_predicted_class; evidence: tonight
+- **audio bins 40–41 (relative progress 0.800–0.840), estimated clip interval 4.07–4.27s** — opposes_predicted_class; evidence: unaligned audio feature rows 78–82; review the linked source clip at the estimated relative span
+- **audio bins 11–12 (relative progress 0.220–0.260), estimated clip interval 1.12–1.32s** — supports_predicted_class; evidence: unaligned audio feature rows 21–25; review the linked source clip at the estimated relative span
+- **vision bins 41–43 (relative progress 0.820–0.880), estimated clip interval 4.17–4.48s** — opposes_predicted_class; evidence: unaligned vision feature rows 60–65; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_08_vision_1.jpg)
+- **vision bins 3–4 (relative progress 0.060–0.100), estimated clip interval 0.31–0.51s** — opposes_predicted_class; evidence: unaligned vision feature rows 4–7; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_08_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E0_EarlyConcat/08.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.6036 / +1.7624 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: -0.4005 / -0.3419. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +1.3297.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+That brings us to tonight, the Universal Design Grand Challenge
diff --git a/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/09.md b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/09.md
new file mode 100644
index 0000000..4f91eb0
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/09.md
@@ -0,0 +1,54 @@
+# Q3 explanation card — E0_EarlyConcat — 09
+
+## Prediction
+
+- Polarity: **negative**
+- Intensity: -1.750
+- Predicted-class confidence: 0.920
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/09.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +2.4909 | 0.906 | -2.1191 | 0.988 |
+| audio | -0.0254 | 0.009 | -0.0083 | 0.004 |
+| vision | -0.2337 | 0.085 | -0.0172 | 0.008 |
+
+Shapley completeness residuals: class 0.00e+00, intensity 4.44e-16.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | -0.0573 | +0.0615 |
+| Text + Vision | -0.0030 | +0.1784 |
+| Audio + Vision | -0.0220 | +0.1153 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 28–32 (relative progress 0.560–0.660), estimated clip interval 2.82–3.33s** — supports_predicted_class; evidence: at all,
+- **audio bins 28–32 (relative progress 0.560–0.660), estimated clip interval 2.82–3.33s** — opposes_predicted_class; evidence: unaligned audio feature rows 55–65; review the linked source clip at the estimated relative span
+- **vision bins 6–10 (relative progress 0.120–0.220), estimated clip interval 0.61–1.11s** — opposes_predicted_class; evidence: unaligned vision feature rows 8–16; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_09_vision_1.jpg)
+
+![Local evidence profile](../../evidence_profiles/E0_EarlyConcat/09.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.7794 / +2.2603 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: -0.2221 / -0.3355. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +1.7260.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+(uhh) I did not like this movie at all, I would not recommend it
diff --git a/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/10.md b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/10.md
new file mode 100644
index 0000000..6d45f15
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/10.md
@@ -0,0 +1,57 @@
+# Q3 explanation card — E0_EarlyConcat — 10
+
+## Prediction
+
+- Polarity: **negative**
+- Intensity: -0.954
+- Predicted-class confidence: 0.783
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/10.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +1.7536 | 0.822 | -1.6119 | 0.855 |
+| audio | -0.2526 | 0.118 | +0.2683 | 0.142 |
+| vision | +0.1275 | 0.060 | -0.0050 | 0.003 |
+
+Shapley completeness residuals: class 0.00e+00, intensity 2.22e-16.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | +0.0873 | -0.0349 |
+| Text + Vision | -0.1980 | +0.1134 |
+| Audio + Vision | +0.0271 | +0.0146 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 42–46 (relative progress 0.840–0.940), estimated clip interval 8.35–9.34s** — supports_predicted_class; evidence: this one was pretty terrible
+- **audio bins 3–5 (relative progress 0.060–0.120), estimated clip interval 0.60–1.19s** — opposes_predicted_class; evidence: unaligned audio feature rows 11–23; review the linked source clip at the estimated relative span
+- **audio bins 22–23 (relative progress 0.440–0.480), estimated clip interval 4.37–4.77s** — opposes_predicted_class; evidence: unaligned audio feature rows 85–93; review the linked source clip at the estimated relative span
+- **vision bins 4–5 (relative progress 0.080–0.120), estimated clip interval 0.80–1.19s** — supports_predicted_class; evidence: unaligned vision feature rows 11–17; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_10_vision_1.jpg)
+- **vision bins 23–24 (relative progress 0.460–0.500), estimated clip interval 4.57–4.97s** — supports_predicted_class; evidence: unaligned vision feature rows 67–73; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_10_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E0_EarlyConcat/10.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.8593 / +1.6509 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: -0.8881 / -0.3659. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +1.3542.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+(umm) And you know I really do like to see fluffy chick flicks sometimes so I'm not against that but this one was pretty terrible
diff --git a/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/11.md b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/11.md
new file mode 100644
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--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/11.md
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+# Q3 explanation card — E0_EarlyConcat — 11
+
+## Prediction
+
+- Polarity: **negative**
+- Intensity: -0.469
+- Predicted-class confidence: 0.614
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/11.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +0.6469 | 0.458 | -0.6779 | 0.525 |
+| audio | +0.5563 | 0.394 | -0.4001 | 0.310 |
+| vision | -0.2098 | 0.148 | +0.2138 | 0.165 |
+
+Shapley completeness residuals: class 0.00e+00, intensity 0.00e+00.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | -0.2481 | +0.2153 |
+| Text + Vision | +0.0912 | -0.0661 |
+| Audio + Vision | -0.0434 | +0.0212 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 8–12 (relative progress 0.160–0.260), estimated clip interval 0.93–1.51s** — supports_predicted_class; evidence: would be ashamed
+- **audio bins 32–36 (relative progress 0.640–0.740), estimated clip interval 3.71–4.29s** — supports_predicted_class; evidence: unaligned audio feature rows 72–84; review the linked source clip at the estimated relative span
+- **vision bins 21–24 (relative progress 0.420–0.500), estimated clip interval 2.44–2.90s** — opposes_predicted_class; evidence: unaligned vision feature rows 36–42; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_11_vision_1.jpg)
+- **vision bins 33–33 (relative progress 0.660–0.680), estimated clip interval 3.83–3.95s** — opposes_predicted_class; evidence: unaligned vision feature rows 56–58; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_11_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E0_EarlyConcat/11.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.7060 / +0.7600 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: -1.4195 / -0.1219. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +0.7509.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+I would be ashamed to have made this film if I was a director
diff --git a/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/12.md b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/12.md
new file mode 100644
index 0000000..c2f5877
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/12.md
@@ -0,0 +1,56 @@
+# Q3 explanation card — E0_EarlyConcat — 12
+
+## Prediction
+
+- Polarity: **negative**
+- Intensity: -1.238
+- Predicted-class confidence: 0.848
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/12.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +1.3737 | 0.766 | -1.3134 | 0.805 |
+| audio | +0.0008 | 0.000 | -0.0352 | 0.022 |
+| vision | +0.4181 | 0.233 | -0.2839 | 0.174 |
+
+Shapley completeness residuals: class 0.00e+00, intensity 0.00e+00.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | -0.0123 | +0.0978 |
+| Text + Vision | -0.2289 | +0.2750 |
+| Audio + Vision | -0.1519 | +0.0960 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 26–28 (relative progress 0.520–0.580), estimated clip interval 6.77–7.55s** — supports_predicted_class; evidence: by fault of their
+- **text bins 45–45 (relative progress 0.900–0.920), estimated clip interval 11.71–11.97s** — supports_predicted_class; evidence: is very
+- **audio bins 8–10 (relative progress 0.160–0.220), estimated clip interval 2.08–2.86s** — opposes_predicted_class; evidence: unaligned audio feature rows 41–56; review the linked source clip at the estimated relative span
+- **audio bins 24–24 (relative progress 0.480–0.500), estimated clip interval 6.25–6.51s** — opposes_predicted_class; evidence: unaligned audio feature rows 123–128; review the linked source clip at the estimated relative span
+- **vision bins 3–7 (relative progress 0.060–0.160), estimated clip interval 0.78–2.08s** — supports_predicted_class; evidence: unaligned vision feature rows 11–30; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_12_vision_1.jpg)
+
+![Local evidence profile](../../evidence_profiles/E0_EarlyConcat/12.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.5112 / +1.1841 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: -0.1864 / -0.0623. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +0.9530.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+Or worse, an individual previously had good credit, but usually by no fault of their own, or perhaps by fault of their own, they have let their credit sag, and credit scores is very low.
diff --git a/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/13.md b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/13.md
new file mode 100644
index 0000000..b40e281
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/13.md
@@ -0,0 +1,58 @@
+# Q3 explanation card — E0_EarlyConcat — 13
+
+## Prediction
+
+- Polarity: **neutral**
+- Intensity: -0.056
+- Predicted-class confidence: 0.617
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/13.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +1.3506 | 0.767 | -0.3893 | 0.399 |
+| audio | +0.0614 | 0.035 | -0.3239 | 0.332 |
+| vision | -0.3485 | 0.198 | +0.2621 | 0.269 |
+
+Shapley completeness residuals: class 0.00e+00, intensity 0.00e+00.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | -0.0766 | +0.3903 |
+| Text + Vision | +0.1221 | +0.0147 |
+| Audio + Vision | +0.0191 | +0.1027 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 30–32 (relative progress 0.600–0.660), estimated clip interval 5.21–5.73s** — supports_predicted_class; evidence: a relationship between
+- **text bins 26–27 (relative progress 0.520–0.560), estimated clip interval 4.51–4.86s** — supports_predicted_class; evidence: i can
+- **audio bins 24–25 (relative progress 0.480–0.520), estimated clip interval 4.17–4.51s** — supports_predicted_class; evidence: unaligned audio feature rows 83–89; review the linked source clip at the estimated relative span
+- **audio bins 20–21 (relative progress 0.400–0.440), estimated clip interval 3.47–3.82s** — opposes_predicted_class; evidence: unaligned audio feature rows 69–76; review the linked source clip at the estimated relative span
+- **vision bins 45–47 (relative progress 0.900–0.960), estimated clip interval 7.81–8.33s** — opposes_predicted_class; evidence: unaligned vision feature rows 16–17; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_13_vision_1.jpg)
+- **vision bins 42–43 (relative progress 0.840–0.880), estimated clip interval 7.29–7.64s** — opposes_predicted_class; evidence: unaligned vision feature rows 15–15; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_13_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E0_EarlyConcat/13.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.4948 / +1.2805 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: -0.1418 / -0.2321. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +0.9508.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+For example, I could take a set of data and from that data, I can find a relationship between any two of the given factors or more.
diff --git a/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/14.md b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/14.md
new file mode 100644
index 0000000..024b033
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/14.md
@@ -0,0 +1,57 @@
+# Q3 explanation card — E0_EarlyConcat — 14
+
+## Prediction
+
+- Polarity: **positive**
+- Intensity: +0.767
+- Predicted-class confidence: 0.698
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/14.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | -0.7961 | 0.343 | -0.6639 | 0.391 |
+| audio | +0.3769 | 0.163 | +0.1741 | 0.102 |
+| vision | +1.1452 | 0.494 | +0.8621 | 0.507 |
+
+Shapley completeness residuals: class -2.22e-16, intensity -1.11e-16.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | +0.0732 | +0.1047 |
+| Text + Vision | -0.2140 | -0.0911 |
+| Audio + Vision | -0.1629 | -0.0936 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 33–35 (relative progress 0.660–0.720), estimated clip interval 6.83–7.45s** — opposes_predicted_class; evidence: ( ufe
+- **text bins 28–29 (relative progress 0.560–0.600), estimated clip interval 5.80–6.21s** — opposes_predicted_class; evidence: of uniform final
+- **audio bins 25–29 (relative progress 0.500–0.600), estimated clip interval 5.18–6.21s** — supports_predicted_class; evidence: unaligned audio feature rows 102–122; review the linked source clip at the estimated relative span
+- **vision bins 37–39 (relative progress 0.740–0.800), estimated clip interval 7.66–8.28s** — supports_predicted_class; evidence: unaligned vision feature rows 113–122; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_14_vision_1.jpg)
+- **vision bins 29–30 (relative progress 0.580–0.620), estimated clip interval 6.00–6.42s** — supports_predicted_class; evidence: unaligned vision feature rows 88–94; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_14_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E0_EarlyConcat/14.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: -0.5230 / -0.2394 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +1.9185 / +0.7809. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: -0.1013.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+He is the co-founder of Rossen and Vettese Limited and the former Executive Director of Uniform Final Examination (UFE) courses at Toronto's York University.
diff --git a/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/15.md b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/15.md
new file mode 100644
index 0000000..6038808
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/15.md
@@ -0,0 +1,58 @@
+# Q3 explanation card — E0_EarlyConcat — 15
+
+## Prediction
+
+- Polarity: **positive**
+- Intensity: +1.708
+- Predicted-class confidence: 0.945
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/15.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +1.1609 | 0.579 | +0.7296 | 0.556 |
+| audio | +0.3684 | 0.184 | +0.1344 | 0.102 |
+| vision | +0.4769 | 0.238 | +0.4493 | 0.342 |
+
+Shapley completeness residuals: class -4.44e-16, intensity 0.00e+00.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | -0.2422 | -0.0633 |
+| Text + Vision | -0.3853 | -0.3292 |
+| Audio + Vision | -0.0929 | -0.0623 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 20–22 (relative progress 0.400–0.460), estimated clip interval 2.93–3.37s** — supports_predicted_class; evidence: prioriti
+- **text bins 41–42 (relative progress 0.820–0.860), estimated clip interval 6.01–6.31s** — supports_predicted_class; evidence: power to
+- **audio bins 3–5 (relative progress 0.060–0.120), estimated clip interval 0.44–0.88s** — supports_predicted_class; evidence: unaligned audio feature rows 8–17; review the linked source clip at the estimated relative span
+- **audio bins 11–11 (relative progress 0.220–0.240), estimated clip interval 1.61–1.76s** — supports_predicted_class; evidence: unaligned audio feature rows 31–34; review the linked source clip at the estimated relative span
+- **vision bins 14–16 (relative progress 0.280–0.340), estimated clip interval 2.05–2.49s** — supports_predicted_class; evidence: unaligned vision feature rows 30–37; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_15_vision_1.jpg)
+- **vision bins 49–49 (relative progress 0.980–1.000), estimated clip interval 7.19–7.33s** — supports_predicted_class; evidence: unaligned vision feature rows 107–109; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_15_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E0_EarlyConcat/15.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.2317 / +0.6047 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.0641 / +0.0459. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +0.6060.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+However, despite their poverty, the family prioritize education because they believed in its power to transform lives
diff --git a/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/16.md b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/16.md
new file mode 100644
index 0000000..0db3920
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/16.md
@@ -0,0 +1,58 @@
+# Q3 explanation card — E0_EarlyConcat — 16
+
+## Prediction
+
+- Polarity: **negative**
+- Intensity: -1.797
+- Predicted-class confidence: 0.944
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/16.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +2.1145 | 0.855 | -1.7931 | 0.818 |
+| audio | +0.0127 | 0.005 | -0.0988 | 0.045 |
+| vision | +0.3469 | 0.140 | -0.2997 | 0.137 |
+
+Shapley completeness residuals: class -4.44e-16, intensity 0.00e+00.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | -0.1119 | +0.1550 |
+| Text + Vision | -0.2551 | +0.3214 |
+| Audio + Vision | -0.1040 | +0.1047 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 15–17 (relative progress 0.300–0.360), estimated clip interval 0.93–1.12s** — supports_predicted_class; evidence: s a
+- **text bins 34–35 (relative progress 0.680–0.720), estimated clip interval 2.12–2.24s** — supports_predicted_class; evidence: is a
+- **audio bins 32–34 (relative progress 0.640–0.700), estimated clip interval 1.99–2.18s** — opposes_predicted_class; evidence: unaligned audio feature rows 38–42; review the linked source clip at the estimated relative span
+- **audio bins 12–13 (relative progress 0.240–0.280), estimated clip interval 0.75–0.87s** — opposes_predicted_class; evidence: unaligned audio feature rows 14–16; review the linked source clip at the estimated relative span
+- **vision bins 9–11 (relative progress 0.180–0.240), estimated clip interval 0.56–0.75s** — supports_predicted_class; evidence: unaligned vision feature rows 7–10; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_16_vision_1.jpg)
+- **vision bins 47–48 (relative progress 0.940–0.980), estimated clip interval 2.93–3.05s** — supports_predicted_class; evidence: unaligned vision feature rows 41–43; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_16_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E0_EarlyConcat/16.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.7584 / +1.7979 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.0846 / -0.2086. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +1.3835.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+It's a terrible, this is a terrible movie
diff --git a/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/17.md b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/17.md
new file mode 100644
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--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/17.md
@@ -0,0 +1,58 @@
+# Q3 explanation card — E0_EarlyConcat — 17
+
+## Prediction
+
+- Polarity: **positive**
+- Intensity: +1.779
+- Predicted-class confidence: 0.951
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/17.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +1.7625 | 0.636 | +1.1566 | 0.628 |
+| audio | -0.3269 | 0.118 | -0.2282 | 0.124 |
+| vision | +0.6833 | 0.246 | +0.4559 | 0.248 |
+
+Shapley completeness residuals: class 0.00e+00, intensity 0.00e+00.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | +0.2539 | +0.1653 |
+| Text + Vision | -0.6970 | -0.4395 |
+| Audio + Vision | +0.0401 | +0.0092 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 26–27 (relative progress 0.520–0.560), estimated clip interval 5.03–5.41s** — supports_predicted_class; evidence: and will
+- **text bins 2–2 (relative progress 0.040–0.060), estimated clip interval 0.39–0.58s** — supports_predicted_class; evidence: applying
+- **audio bins 32–33 (relative progress 0.640–0.680), estimated clip interval 6.19–6.57s** — opposes_predicted_class; evidence: unaligned audio feature rows 122–129; review the linked source clip at the estimated relative span
+- **audio bins 2–3 (relative progress 0.040–0.080), estimated clip interval 0.39–0.77s** — opposes_predicted_class; evidence: unaligned audio feature rows 7–15; review the linked source clip at the estimated relative span
+- **vision bins 46–49 (relative progress 0.920–1.000), estimated clip interval 8.89–9.67s** — supports_predicted_class; evidence: unaligned vision feature rows 131–142; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_17_vision_1.jpg)
+- **vision bins 29–29 (relative progress 0.580–0.600), estimated clip interval 5.61–5.80s** — supports_predicted_class; evidence: unaligned vision feature rows 82–85; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_17_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E0_EarlyConcat/17.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.3403 / +1.1632 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.2587 / -0.0617. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +0.8566.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+Applying these four design concepts to your presentations is simple, easy and will make people think you turned into a design guru.
diff --git a/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/18.md b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/18.md
new file mode 100644
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--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/18.md
@@ -0,0 +1,57 @@
+# Q3 explanation card — E0_EarlyConcat — 18
+
+## Prediction
+
+- Polarity: **neutral**
+- Intensity: -0.379
+- Predicted-class confidence: 0.507
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/18.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +0.5864 | 0.482 | -0.3991 | 0.487 |
+| audio | -0.2698 | 0.222 | +0.0225 | 0.028 |
+| vision | +0.3607 | 0.296 | -0.3975 | 0.485 |
+
+Shapley completeness residuals: class 0.00e+00, intensity 0.00e+00.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | +0.0485 | -0.0700 |
+| Text + Vision | -0.1951 | +0.1509 |
+| Audio + Vision | -0.0033 | +0.2356 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 6–10 (relative progress 0.120–0.220), estimated clip interval 2.26–4.14s** — supports_predicted_class; evidence: - the first baltic cod fishery
+- **audio bins 13–14 (relative progress 0.260–0.300), estimated clip interval 4.89–5.64s** — opposes_predicted_class; evidence: unaligned audio feature rows 96–111; review the linked source clip at the estimated relative span
+- **audio bins 1–2 (relative progress 0.020–0.060), estimated clip interval 0.38–1.13s** — opposes_predicted_class; evidence: unaligned audio feature rows 7–22; review the linked source clip at the estimated relative span
+- **vision bins 18–20 (relative progress 0.360–0.420), estimated clip interval 6.77–7.90s** — supports_predicted_class; evidence: unaligned vision feature rows 100–117; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_18_vision_1.jpg)
+- **vision bins 40–41 (relative progress 0.800–0.840), estimated clip interval 15.05–15.80s** — supports_predicted_class; evidence: unaligned vision feature rows 224–235; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_18_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E0_EarlyConcat/18.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.2234 / +0.4210 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: -0.2583 / -0.1544. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +0.3293.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+-And in Denmark - the first Baltic Cod fishery has been MSC – certified -Meanwhile, the Faeroese Mackerel Fishery has been denied MSC certification based on the fact that the fishery has failed to reach an agreement on mackerel quotas with Norway and the European Union.
diff --git a/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/19.md b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/19.md
new file mode 100644
index 0000000..2bb1681
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/19.md
@@ -0,0 +1,58 @@
+# Q3 explanation card — E0_EarlyConcat — 19
+
+## Prediction
+
+- Polarity: **positive**
+- Intensity: +0.030
+- Predicted-class confidence: 0.496
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/19.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | -0.0481 | 0.042 | -0.4154 | 0.367 |
+| audio | -0.3937 | 0.346 | -0.3329 | 0.294 |
+| vision | +0.6959 | 0.612 | +0.3834 | 0.339 |
+
+Shapley completeness residuals: class -5.55e-17, intensity 0.00e+00.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | +0.1825 | +0.1441 |
+| Text + Vision | -0.2654 | -0.0422 |
+| Audio + Vision | +0.0285 | +0.0474 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 35–36 (relative progress 0.700–0.740), estimated clip interval 9.58–10.13s** — opposes_predicted_class; evidence: over imperfections
+- **text bins 40–41 (relative progress 0.800–0.840), estimated clip interval 10.95–11.49s** — supports_predicted_class; evidence: but for the
+- **audio bins 16–18 (relative progress 0.320–0.380), estimated clip interval 4.38–5.20s** — opposes_predicted_class; evidence: unaligned audio feature rows 87–103; review the linked source clip at the estimated relative span
+- **audio bins 0–1 (relative progress 0.000–0.040), estimated clip interval 0.00–0.55s** — opposes_predicted_class; evidence: unaligned audio feature rows 0–10; review the linked source clip at the estimated relative span
+- **vision bins 31–33 (relative progress 0.620–0.680), estimated clip interval 8.48–9.30s** — supports_predicted_class; evidence: unaligned vision feature rows 126–138; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_19_vision_1.jpg)
+- **vision bins 27–28 (relative progress 0.540–0.580), estimated clip interval 7.39–7.94s** — supports_predicted_class; evidence: unaligned vision feature rows 110–118; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_19_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E0_EarlyConcat/19.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: -0.1179 / +0.0719 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.4099 / +0.0560. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +0.0836.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+People are surprisingly forgiving brands when they own up to mistakes, and unfortunately some haters out there love to point fingers and jump all over imperfections, but for the most part, people understand
diff --git a/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/20.md b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/20.md
new file mode 100644
index 0000000..43c4ed3
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E0_EarlyConcat/20.md
@@ -0,0 +1,58 @@
+# Q3 explanation card — E0_EarlyConcat — 20
+
+## Prediction
+
+- Polarity: **positive**
+- Intensity: +1.269
+- Predicted-class confidence: 0.865
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/20.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +1.7774 | 0.829 | +1.2266 | 0.738 |
+| audio | -0.3541 | 0.165 | -0.3939 | 0.237 |
+| vision | +0.0132 | 0.006 | +0.0415 | 0.025 |
+
+Shapley completeness residuals: class -2.22e-16, intensity -1.11e-16.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | +0.3277 | +0.3908 |
+| Text + Vision | -0.0568 | +0.0263 |
+| Audio + Vision | +0.0454 | +0.0601 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 24–26 (relative progress 0.480–0.540), estimated clip interval 5.26–5.91s** — supports_predicted_class; evidence: ll have more live
+- **text bins 11–12 (relative progress 0.220–0.260), estimated clip interval 2.41–2.85s** — supports_predicted_class; evidence: of the description
+- **audio bins 33–34 (relative progress 0.660–0.700), estimated clip interval 7.23–7.67s** — opposes_predicted_class; evidence: unaligned audio feature rows 142–151; review the linked source clip at the estimated relative span
+- **audio bins 41–42 (relative progress 0.820–0.860), estimated clip interval 8.98–9.42s** — opposes_predicted_class; evidence: unaligned audio feature rows 177–185; review the linked source clip at the estimated relative span
+- **vision bins 42–44 (relative progress 0.840–0.900), estimated clip interval 9.20–9.86s** — supports_predicted_class; evidence: unaligned vision feature rows 136–145; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_20_vision_1.jpg)
+- **vision bins 15–16 (relative progress 0.300–0.340), estimated clip interval 3.29–3.72s** — opposes_predicted_class; evidence: unaligned vision feature rows 48–55; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E0_EarlyConcat_20_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E0_EarlyConcat/20.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.5846 / +1.8524 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: -0.0170 / -0.1314. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +1.5152.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+And of course, click in the link of the description of this video for more, and we'll have more live updates and a stock market video (wrap-up) at the end of the day today.
diff --git a/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/01.md b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/01.md
new file mode 100644
index 0000000..2ae25f6
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/01.md
@@ -0,0 +1,50 @@
+# Q3 explanation card — E1_MoFE_Router — 01
+
+## Prediction
+
+- Polarity: **neutral**
+- Intensity: +0.251
+- Predicted-class confidence: 0.477
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/01.mp4
+
+## Router profile (intrinsic routing signal, not prediction contribution)
+
+| Modality | Router exposure share | Exact Shapley absolute share |
+|---|---:|---:|
+| text | 0.363 | 0.642 |
+| audio | 0.294 | 0.139 |
+| vision | 0.343 | 0.219 |
+
+Router–Shapley Spearman: 1.0; top modality agreement: True.
+Router values describe mixture routing. The counterfactual scores below test whether that routing signal tracks model behavior.
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 35–36 (relative progress 0.700–0.740), estimated clip interval 6.30–6.66s** — router_activity_not_signed_contribution; evidence: belt performs
+- **text bins 4–4 (relative progress 0.080–0.100), estimated clip interval 0.72–0.90s** — router_activity_not_signed_contribution; evidence: replacing these
+- **audio bins 32–32 (relative progress 0.640–0.660), estimated clip interval 5.76–5.94s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 114–118; review the linked source clip at the estimated relative span
+- **audio bins 35–35 (relative progress 0.700–0.720), estimated clip interval 6.30–6.48s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 125–128; review the linked source clip at the estimated relative span
+- **vision bins 4–5 (relative progress 0.080–0.120), estimated clip interval 0.72–1.08s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 10–16; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_01_vision_1.jpg)
+- **vision bins 35–36 (relative progress 0.700–0.740), estimated clip interval 6.30–6.66s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 94–99; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_01_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E1_MoFE_Router/01.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.3703 / +1.0939 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.0722 / -0.1165. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +0.8309.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+Replacing these wear components when replacing the timing belt is essential to ensuring the new belt performs to its mileage requirements
diff --git a/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/02.md b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/02.md
new file mode 100644
index 0000000..f8cfa24
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/02.md
@@ -0,0 +1,50 @@
+# Q3 explanation card — E1_MoFE_Router — 02
+
+## Prediction
+
+- Polarity: **neutral**
+- Intensity: +0.110
+- Predicted-class confidence: 0.462
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/02.mp4
+
+## Router profile (intrinsic routing signal, not prediction contribution)
+
+| Modality | Router exposure share | Exact Shapley absolute share |
+|---|---:|---:|
+| text | 0.358 | 0.723 |
+| audio | 0.289 | 0.055 |
+| vision | 0.352 | 0.222 |
+
+Router–Shapley Spearman: 1.0; top modality agreement: True.
+Router values describe mixture routing. The counterfactual scores below test whether that routing signal tracks model behavior.
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 0–3 (relative progress 0.000–0.080), estimated clip interval 0.00–0.28s** — router_activity_not_signed_contribution; evidence: we
+- **text bins 48–48 (relative progress 0.960–0.980), estimated clip interval 3.35–3.42s** — router_activity_not_signed_contribution; evidence: We want to live by each other’s happiness - not by each other’s misery.
+- **audio bins 19–20 (relative progress 0.380–0.420), estimated clip interval 1.33–1.47s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 25–27; review the linked source clip at the estimated relative span
+- **audio bins 9–10 (relative progress 0.180–0.220), estimated clip interval 0.63–0.77s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 11–14; review the linked source clip at the estimated relative span
+- **vision bins 48–49 (relative progress 0.960–1.000), estimated clip interval 3.35–3.49s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 48–49; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_02_vision_1.jpg)
+- **vision bins 1–2 (relative progress 0.020–0.060), estimated clip interval 0.07–0.21s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 1–2; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_02_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E1_MoFE_Router/02.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.2126 / +0.9516 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.1482 / +0.1970. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +0.6623.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+We want to live by each other’s happiness - not by each other’s misery.
diff --git a/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/03.md b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/03.md
new file mode 100644
index 0000000..6eb45ed
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/03.md
@@ -0,0 +1,50 @@
+# Q3 explanation card — E1_MoFE_Router — 03
+
+## Prediction
+
+- Polarity: **negative**
+- Intensity: -0.610
+- Predicted-class confidence: 0.688
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/03.mp4
+
+## Router profile (intrinsic routing signal, not prediction contribution)
+
+| Modality | Router exposure share | Exact Shapley absolute share |
+|---|---:|---:|
+| text | 0.359 | 0.723 |
+| audio | 0.292 | 0.011 |
+| vision | 0.349 | 0.266 |
+
+Router–Shapley Spearman: 1.0; top modality agreement: True.
+Router values describe mixture routing. The counterfactual scores below test whether that routing signal tracks model behavior.
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 33–34 (relative progress 0.660–0.700), estimated clip interval 6.38–6.76s** — router_activity_not_signed_contribution; evidence: income into
+- **text bins 9–9 (relative progress 0.180–0.200), estimated clip interval 1.74–1.93s** — router_activity_not_signed_contribution; evidence: ##er
+- **audio bins 31–34 (relative progress 0.620–0.700), estimated clip interval 5.99–6.76s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 118–133; review the linked source clip at the estimated relative span
+- **audio bins 0–0 (relative progress 0.000–0.020), estimated clip interval 0.00–0.19s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 0–3; review the linked source clip at the estimated relative span
+- **vision bins 37–39 (relative progress 0.740–0.800), estimated clip interval 7.15–7.73s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 106–115; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_03_vision_1.jpg)
+- **vision bins 48–48 (relative progress 0.960–0.980), estimated clip interval 9.28–9.47s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 138–141; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_03_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E1_MoFE_Router/03.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.3234 / +1.2305 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.1027 / +0.1196. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +0.8736.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+There's one lender at the moment which I think is just Bankwest who don't take rental income into account, they take rental yield into account.
diff --git a/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/04.md b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/04.md
new file mode 100644
index 0000000..6d389a7
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/04.md
@@ -0,0 +1,50 @@
+# Q3 explanation card — E1_MoFE_Router — 04
+
+## Prediction
+
+- Polarity: **negative**
+- Intensity: -0.942
+- Predicted-class confidence: 0.784
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/04.mp4
+
+## Router profile (intrinsic routing signal, not prediction contribution)
+
+| Modality | Router exposure share | Exact Shapley absolute share |
+|---|---:|---:|
+| text | 0.359 | 0.789 |
+| audio | 0.291 | 0.196 |
+| vision | 0.349 | 0.015 |
+
+Router–Shapley Spearman: 0.5; top modality agreement: True.
+Router values describe mixture routing. The counterfactual scores below test whether that routing signal tracks model behavior.
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 44–47 (relative progress 0.880–0.960), estimated clip interval 6.15–6.71s** — router_activity_not_signed_contribution; evidence: absolutely not.
+- **text bins 21–21 (relative progress 0.420–0.440), estimated clip interval 2.94–3.08s** — router_activity_not_signed_contribution; evidence: i kiss
+- **audio bins 13–15 (relative progress 0.260–0.320), estimated clip interval 1.82–2.24s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 35–43; review the linked source clip at the estimated relative span
+- **audio bins 46–46 (relative progress 0.920–0.940), estimated clip interval 6.43–6.57s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 126–128; review the linked source clip at the estimated relative span
+- **vision bins 39–42 (relative progress 0.780–0.860), estimated clip interval 5.46–6.01s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 80–88; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_04_vision_1.jpg)
+- **vision bins 7–7 (relative progress 0.140–0.160), estimated clip interval 0.98–1.12s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 14–16; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_04_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E1_MoFE_Router/04.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.3890 / +1.5730 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.4015 / +0.1569. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +1.4743.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+If I blow it at the team exercise, should I kiss my chances of cheering "GO BLUE" goodbye?] Absolutely not.
diff --git a/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/05.md b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/05.md
new file mode 100644
index 0000000..5bd2617
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/05.md
@@ -0,0 +1,50 @@
+# Q3 explanation card — E1_MoFE_Router — 05
+
+## Prediction
+
+- Polarity: **positive**
+- Intensity: +0.871
+- Predicted-class confidence: 0.837
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/05.mp4
+
+## Router profile (intrinsic routing signal, not prediction contribution)
+
+| Modality | Router exposure share | Exact Shapley absolute share |
+|---|---:|---:|
+| text | 0.359 | 0.456 |
+| audio | 0.291 | 0.111 |
+| vision | 0.350 | 0.433 |
+
+Router–Shapley Spearman: 1.0; top modality agreement: True.
+Router values describe mixture routing. The counterfactual scores below test whether that routing signal tracks model behavior.
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 43–45 (relative progress 0.860–0.920), estimated clip interval 5.25–5.61s** — router_activity_not_signed_contribution; evidence: marketing.
+- **text bins 0–0 (relative progress 0.000–0.020), estimated clip interval 0.00–0.12s** — router_activity_not_signed_contribution; evidence: Hi, my name is Chloe, video marketer for Red Wagon Marketing.
+- **audio bins 43–46 (relative progress 0.860–0.940), estimated clip interval 5.25–5.73s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 103–112; review the linked source clip at the estimated relative span
+- **audio bins 23–23 (relative progress 0.460–0.480), estimated clip interval 2.81–2.93s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 55–57; review the linked source clip at the estimated relative span
+- **vision bins 3–5 (relative progress 0.060–0.120), estimated clip interval 0.37–0.73s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 5–10; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_05_vision_1.jpg)
+- **vision bins 17–17 (relative progress 0.340–0.360), estimated clip interval 2.07–2.20s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 30–32; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_05_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E1_MoFE_Router/05.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.0746 / +0.1400 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.9288 / +0.5481. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +0.4152.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+Hi, my name is Chloe, video marketer for Red Wagon Marketing.
diff --git a/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/06.md b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/06.md
new file mode 100644
index 0000000..ee11ad4
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/06.md
@@ -0,0 +1,50 @@
+# Q3 explanation card — E1_MoFE_Router — 06
+
+## Prediction
+
+- Polarity: **positive**
+- Intensity: +0.790
+- Predicted-class confidence: 0.805
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/06.mp4
+
+## Router profile (intrinsic routing signal, not prediction contribution)
+
+| Modality | Router exposure share | Exact Shapley absolute share |
+|---|---:|---:|
+| text | 0.359 | 0.801 |
+| audio | 0.290 | 0.121 |
+| vision | 0.351 | 0.078 |
+
+Router–Shapley Spearman: 0.5; top modality agreement: True.
+Router values describe mixture routing. The counterfactual scores below test whether that routing signal tracks model behavior.
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 40–40 (relative progress 0.800–0.820), estimated clip interval 6.64–6.81s** — router_activity_not_signed_contribution; evidence: check out
+- **text bins 0–0 (relative progress 0.000–0.020), estimated clip interval 0.00–0.17s** — router_activity_not_signed_contribution; evidence: If you're a fan of dancing in that sense, just like to watch people dance, see impressive dance moves then you might want to check out this movie solely for that
+- **audio bins 43–44 (relative progress 0.860–0.900), estimated clip interval 7.14–7.47s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 140–146; review the linked source clip at the estimated relative span
+- **audio bins 39–40 (relative progress 0.780–0.820), estimated clip interval 6.48–6.81s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 127–133; review the linked source clip at the estimated relative span
+- **vision bins 46–49 (relative progress 0.920–1.000), estimated clip interval 7.64–8.31s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 113–122; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_06_vision_1.jpg)
+- **vision bins 13–13 (relative progress 0.260–0.280), estimated clip interval 2.16–2.33s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 31–34; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_06_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E1_MoFE_Router/06.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.5262 / +1.6266 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.4002 / -0.1076. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +1.3422.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+If you're a fan of dancing in that sense, just like to watch people dance, see impressive dance moves then you might want to check out this movie solely for that
diff --git a/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/07.md b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/07.md
new file mode 100644
index 0000000..ee052af
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/07.md
@@ -0,0 +1,50 @@
+# Q3 explanation card — E1_MoFE_Router — 07
+
+## Prediction
+
+- Polarity: **positive**
+- Intensity: +0.580
+- Predicted-class confidence: 0.734
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/07.mp4
+
+## Router profile (intrinsic routing signal, not prediction contribution)
+
+| Modality | Router exposure share | Exact Shapley absolute share |
+|---|---:|---:|
+| text | 0.362 | 0.838 |
+| audio | 0.294 | 0.153 |
+| vision | 0.344 | 0.009 |
+
+Router–Shapley Spearman: 0.5; top modality agreement: True.
+Router values describe mixture routing. The counterfactual scores below test whether that routing signal tracks model behavior.
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 31–32 (relative progress 0.620–0.660), estimated clip interval 13.24–14.09s** — router_activity_not_signed_contribution; evidence: future of
+- **text bins 0–0 (relative progress 0.000–0.020), estimated clip interval 0.00–0.43s** — router_activity_not_signed_contribution; evidence: As Linn’s associate editor Michael Baadke reports in our November 28 issue, attendees “enthusiastically discussed the topics of growing the hobby, the future of stamp shows, and dealers and philatelic partnerships, along with ways the leading organizations involved in the stamp hobby can work together to make it succeed and grow
+- **audio bins 28–29 (relative progress 0.560–0.600), estimated clip interval 11.95–12.81s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 238–254; review the linked source clip at the estimated relative span
+- **audio bins 34–35 (relative progress 0.680–0.720), estimated clip interval 14.52–15.37s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 289–305; review the linked source clip at the estimated relative span
+- **vision bins 18–19 (relative progress 0.360–0.400), estimated clip interval 7.69–8.54s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 114–127; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_07_vision_1.jpg)
+- **vision bins 36–37 (relative progress 0.720–0.760), estimated clip interval 15.37–16.22s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 229–242; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_07_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E1_MoFE_Router/07.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.1449 / +0.7703 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.4447 / +0.0732. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +0.6005.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+As Linn’s associate editor Michael Baadke reports in our November 28 issue, attendees “enthusiastically discussed the topics of growing the hobby, the future of stamp shows, and dealers and philatelic partnerships, along with ways the leading organizations involved in the stamp hobby can work together to make it succeed and grow
diff --git a/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/08.md b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/08.md
new file mode 100644
index 0000000..30e71a5
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/08.md
@@ -0,0 +1,49 @@
+# Q3 explanation card — E1_MoFE_Router — 08
+
+## Prediction
+
+- Polarity: **positive**
+- Intensity: +0.712
+- Predicted-class confidence: 0.812
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/08.mp4
+
+## Router profile (intrinsic routing signal, not prediction contribution)
+
+| Modality | Router exposure share | Exact Shapley absolute share |
+|---|---:|---:|
+| text | 0.363 | 0.888 |
+| audio | 0.294 | 0.090 |
+| vision | 0.343 | 0.022 |
+
+Router–Shapley Spearman: 0.5; top modality agreement: True.
+Router values describe mixture routing. The counterfactual scores below test whether that routing signal tracks model behavior.
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 42–43 (relative progress 0.840–0.880), estimated clip interval 4.27–4.48s** — router_activity_not_signed_contribution; evidence: grand challenge
+- **text bins 47–48 (relative progress 0.940–0.980), estimated clip interval 4.78–4.98s** — router_activity_not_signed_contribution; evidence: That brings us to tonight, the Universal Design Grand Challenge
+- **audio bins 23–27 (relative progress 0.460–0.560), estimated clip interval 2.34–2.85s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 45–54; review the linked source clip at the estimated relative span
+- **vision bins 47–49 (relative progress 0.940–1.000), estimated clip interval 4.78–5.09s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 69–73; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_08_vision_1.jpg)
+- **vision bins 42–43 (relative progress 0.840–0.880), estimated clip interval 4.27–4.48s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 62–65; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_08_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E1_MoFE_Router/08.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.0763 / +1.0180 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.8223 / +0.0351. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +0.8006.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+That brings us to tonight, the Universal Design Grand Challenge
diff --git a/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/09.md b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/09.md
new file mode 100644
index 0000000..89e3929
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/09.md
@@ -0,0 +1,50 @@
+# Q3 explanation card — E1_MoFE_Router — 09
+
+## Prediction
+
+- Polarity: **negative**
+- Intensity: -1.848
+- Predicted-class confidence: 0.918
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/09.mp4
+
+## Router profile (intrinsic routing signal, not prediction contribution)
+
+| Modality | Router exposure share | Exact Shapley absolute share |
+|---|---:|---:|
+| text | 0.359 | 0.940 |
+| audio | 0.291 | 0.052 |
+| vision | 0.350 | 0.008 |
+
+Router–Shapley Spearman: 0.5; top modality agreement: True.
+Router values describe mixture routing. The counterfactual scores below test whether that routing signal tracks model behavior.
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 8–10 (relative progress 0.160–0.220), estimated clip interval 0.81–1.11s** — router_activity_not_signed_contribution; evidence: ##h )
+- **text bins 4–4 (relative progress 0.080–0.100), estimated clip interval 0.40–0.50s** — router_activity_not_signed_contribution; evidence: (
+- **audio bins 8–10 (relative progress 0.160–0.220), estimated clip interval 0.81–1.11s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 15–21; review the linked source clip at the estimated relative span
+- **audio bins 2–3 (relative progress 0.040–0.080), estimated clip interval 0.20–0.40s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 3–7; review the linked source clip at the estimated relative span
+- **vision bins 25–27 (relative progress 0.500–0.560), estimated clip interval 2.52–2.82s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 37–41; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_09_vision_1.jpg)
+- **vision bins 12–12 (relative progress 0.240–0.260), estimated clip interval 1.21–1.31s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 18–19; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_09_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E1_MoFE_Router/09.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.3663 / +1.5409 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.4417 / +0.2561. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +1.3679.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+(uhh) I did not like this movie at all, I would not recommend it
diff --git a/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/10.md b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/10.md
new file mode 100644
index 0000000..7e1273e
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/10.md
@@ -0,0 +1,50 @@
+# Q3 explanation card — E1_MoFE_Router — 10
+
+## Prediction
+
+- Polarity: **negative**
+- Intensity: -1.260
+- Predicted-class confidence: 0.862
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/10.mp4
+
+## Router profile (intrinsic routing signal, not prediction contribution)
+
+| Modality | Router exposure share | Exact Shapley absolute share |
+|---|---:|---:|
+| text | 0.359 | 0.665 |
+| audio | 0.290 | 0.213 |
+| vision | 0.351 | 0.123 |
+
+Router–Shapley Spearman: 0.5; top modality agreement: True.
+Router values describe mixture routing. The counterfactual scores below test whether that routing signal tracks model behavior.
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 5–7 (relative progress 0.100–0.160), estimated clip interval 0.99–1.59s** — router_activity_not_signed_contribution; evidence: ) and you
+- **text bins 48–48 (relative progress 0.960–0.980), estimated clip interval 9.54–9.74s** — router_activity_not_signed_contribution; evidence: terrible
+- **audio bins 5–6 (relative progress 0.100–0.140), estimated clip interval 0.99–1.39s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 19–27; review the linked source clip at the estimated relative span
+- **audio bins 28–29 (relative progress 0.560–0.600), estimated clip interval 5.57–5.96s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 109–116; review the linked source clip at the estimated relative span
+- **vision bins 10–11 (relative progress 0.200–0.240), estimated clip interval 1.99–2.39s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 29–35; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_10_vision_1.jpg)
+- **vision bins 23–23 (relative progress 0.460–0.480), estimated clip interval 4.57–4.77s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 67–70; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_10_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E1_MoFE_Router/10.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.3188 / +1.7735 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.7996 / +0.2699. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +1.7536.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+(umm) And you know I really do like to see fluffy chick flicks sometimes so I'm not against that but this one was pretty terrible
diff --git a/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/11.md b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/11.md
new file mode 100644
index 0000000..c16d029
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/11.md
@@ -0,0 +1,50 @@
+# Q3 explanation card — E1_MoFE_Router — 11
+
+## Prediction
+
+- Polarity: **negative**
+- Intensity: -0.492
+- Predicted-class confidence: 0.577
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/11.mp4
+
+## Router profile (intrinsic routing signal, not prediction contribution)
+
+| Modality | Router exposure share | Exact Shapley absolute share |
+|---|---:|---:|
+| text | 0.360 | 0.543 |
+| audio | 0.288 | 0.133 |
+| vision | 0.352 | 0.325 |
+
+Router–Shapley Spearman: 1.0; top modality agreement: True.
+Router values describe mixture routing. The counterfactual scores below test whether that routing signal tracks model behavior.
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 31–34 (relative progress 0.620–0.700), estimated clip interval 3.60–4.06s** — router_activity_not_signed_contribution; evidence: film if i
+- **text bins 40–40 (relative progress 0.800–0.820), estimated clip interval 4.64–4.76s** — router_activity_not_signed_contribution; evidence: was a
+- **audio bins 1–2 (relative progress 0.020–0.060), estimated clip interval 0.12–0.35s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 2–6; review the linked source clip at the estimated relative span
+- **audio bins 5–5 (relative progress 0.100–0.120), estimated clip interval 0.58–0.70s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 11–13; review the linked source clip at the estimated relative span
+- **vision bins 32–33 (relative progress 0.640–0.680), estimated clip interval 3.71–3.95s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 55–58; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_11_vision_1.jpg)
+- **vision bins 7–7 (relative progress 0.140–0.160), estimated clip interval 0.81–0.93s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 12–13; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_11_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E1_MoFE_Router/11.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: -0.1744 / +0.4081 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.8332 / -0.1700. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +0.3823.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+I would be ashamed to have made this film if I was a director
diff --git a/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/12.md b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/12.md
new file mode 100644
index 0000000..772aaac
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/12.md
@@ -0,0 +1,50 @@
+# Q3 explanation card — E1_MoFE_Router — 12
+
+## Prediction
+
+- Polarity: **negative**
+- Intensity: -1.088
+- Predicted-class confidence: 0.814
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/12.mp4
+
+## Router profile (intrinsic routing signal, not prediction contribution)
+
+| Modality | Router exposure share | Exact Shapley absolute share |
+|---|---:|---:|
+| text | 0.363 | 0.518 |
+| audio | 0.291 | 0.198 |
+| vision | 0.346 | 0.284 |
+
+Router–Shapley Spearman: 1.0; top modality agreement: True.
+Router values describe mixture routing. The counterfactual scores below test whether that routing signal tracks model behavior.
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 31–33 (relative progress 0.620–0.680), estimated clip interval 8.07–8.85s** — router_activity_not_signed_contribution; evidence: , they have
+- **text bins 40–41 (relative progress 0.800–0.840), estimated clip interval 10.41–10.93s** — router_activity_not_signed_contribution; evidence: , and
+- **audio bins 10–11 (relative progress 0.200–0.240), estimated clip interval 2.60–3.12s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 51–61; review the linked source clip at the estimated relative span
+- **audio bins 28–28 (relative progress 0.560–0.580), estimated clip interval 7.29–7.55s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 143–149; review the linked source clip at the estimated relative span
+- **vision bins 31–33 (relative progress 0.620–0.680), estimated clip interval 8.07–8.85s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 119–131; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_12_vision_1.jpg)
+- **vision bins 40–41 (relative progress 0.800–0.840), estimated clip interval 10.41–10.93s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 154–162; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_12_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E1_MoFE_Router/12.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.1360 / +0.8681 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.3464 / +0.3088. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +1.0633.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+Or worse, an individual previously had good credit, but usually by no fault of their own, or perhaps by fault of their own, they have let their credit sag, and credit scores is very low.
diff --git a/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/13.md b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/13.md
new file mode 100644
index 0000000..c71406d
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/13.md
@@ -0,0 +1,49 @@
+# Q3 explanation card — E1_MoFE_Router — 13
+
+## Prediction
+
+- Polarity: **positive**
+- Intensity: +0.421
+- Predicted-class confidence: 0.525
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/13.mp4
+
+## Router profile (intrinsic routing signal, not prediction contribution)
+
+| Modality | Router exposure share | Exact Shapley absolute share |
+|---|---:|---:|
+| text | 0.363 | 0.515 |
+| audio | 0.296 | 0.126 |
+| vision | 0.340 | 0.359 |
+
+Router–Shapley Spearman: 1.0; top modality agreement: True.
+Router values describe mixture routing. The counterfactual scores below test whether that routing signal tracks model behavior.
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 3–4 (relative progress 0.060–0.100), estimated clip interval 0.52–0.87s** — router_activity_not_signed_contribution; evidence: for example,
+- **text bins 14–15 (relative progress 0.280–0.320), estimated clip interval 2.43–2.78s** — router_activity_not_signed_contribution; evidence: set of data
+- **audio bins 1–5 (relative progress 0.020–0.120), estimated clip interval 0.17–1.04s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 3–20; review the linked source clip at the estimated relative span
+- **vision bins 14–15 (relative progress 0.280–0.320), estimated clip interval 2.43–2.78s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 5–5; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_13_vision_1.jpg)
+- **vision bins 20–20 (relative progress 0.400–0.420), estimated clip interval 3.47–3.65s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 7–7; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_13_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E1_MoFE_Router/13.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.0939 / +0.2419 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: -0.0028 / -0.1247. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +0.2177.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+For example, I could take a set of data and from that data, I can find a relationship between any two of the given factors or more.
diff --git a/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/14.md b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/14.md
new file mode 100644
index 0000000..3448969
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/14.md
@@ -0,0 +1,50 @@
+# Q3 explanation card — E1_MoFE_Router — 14
+
+## Prediction
+
+- Polarity: **positive**
+- Intensity: +0.526
+- Predicted-class confidence: 0.582
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/14.mp4
+
+## Router profile (intrinsic routing signal, not prediction contribution)
+
+| Modality | Router exposure share | Exact Shapley absolute share |
+|---|---:|---:|
+| text | 0.360 | 0.115 |
+| audio | 0.292 | 0.177 |
+| vision | 0.348 | 0.708 |
+
+Router–Shapley Spearman: -0.5; top modality agreement: False.
+Router values describe mixture routing. The counterfactual scores below test whether that routing signal tracks model behavior.
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 0–2 (relative progress 0.000–0.060), estimated clip interval 0.00–0.62s** — router_activity_not_signed_contribution; evidence: he is
+- **text bins 46–46 (relative progress 0.920–0.940), estimated clip interval 9.52–9.73s** — router_activity_not_signed_contribution; evidence: university
+- **audio bins 0–2 (relative progress 0.000–0.060), estimated clip interval 0.00–0.62s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 0–12; review the linked source clip at the estimated relative span
+- **audio bins 46–46 (relative progress 0.920–0.940), estimated clip interval 9.52–9.73s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 187–191; review the linked source clip at the estimated relative span
+- **vision bins 5–7 (relative progress 0.100–0.160), estimated clip interval 1.04–1.66s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 15–24; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_14_vision_1.jpg)
+- **vision bins 32–32 (relative progress 0.640–0.660), estimated clip interval 6.62–6.83s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 97–100; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_14_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E1_MoFE_Router/14.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.0264 / -0.3809 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.4573 / +0.3264. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: -0.2121.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+He is the co-founder of Rossen and Vettese Limited and the former Executive Director of Uniform Final Examination (UFE) courses at Toronto's York University.
diff --git a/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/15.md b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/15.md
new file mode 100644
index 0000000..95c0683
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/15.md
@@ -0,0 +1,50 @@
+# Q3 explanation card — E1_MoFE_Router — 15
+
+## Prediction
+
+- Polarity: **positive**
+- Intensity: +1.053
+- Predicted-class confidence: 0.893
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/15.mp4
+
+## Router profile (intrinsic routing signal, not prediction contribution)
+
+| Modality | Router exposure share | Exact Shapley absolute share |
+|---|---:|---:|
+| text | 0.359 | 0.496 |
+| audio | 0.292 | 0.244 |
+| vision | 0.349 | 0.260 |
+
+Router–Shapley Spearman: 1.0; top modality agreement: True.
+Router values describe mixture routing. The counterfactual scores below test whether that routing signal tracks model behavior.
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 15–16 (relative progress 0.300–0.340), estimated clip interval 2.20–2.49s** — router_activity_not_signed_contribution; evidence: , the
+- **text bins 27–28 (relative progress 0.540–0.580), estimated clip interval 3.96–4.25s** — router_activity_not_signed_contribution; evidence: education because
+- **audio bins 8–9 (relative progress 0.160–0.200), estimated clip interval 1.17–1.47s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 23–28; review the linked source clip at the estimated relative span
+- **audio bins 15–15 (relative progress 0.300–0.320), estimated clip interval 2.20–2.35s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 43–46; review the linked source clip at the estimated relative span
+- **vision bins 47–48 (relative progress 0.940–0.980), estimated clip interval 6.89–7.19s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 103–107; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_15_vision_1.jpg)
+- **vision bins 23–24 (relative progress 0.460–0.500), estimated clip interval 3.37–3.67s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 50–54; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_15_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E1_MoFE_Router/15.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.1767 / +0.5629 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.9233 / +0.5012. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +0.4848.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+However, despite their poverty, the family prioritize education because they believed in its power to transform lives
diff --git a/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/16.md b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/16.md
new file mode 100644
index 0000000..6b6411a
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/16.md
@@ -0,0 +1,49 @@
+# Q3 explanation card — E1_MoFE_Router — 16
+
+## Prediction
+
+- Polarity: **negative**
+- Intensity: -1.836
+- Predicted-class confidence: 0.914
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/16.mp4
+
+## Router profile (intrinsic routing signal, not prediction contribution)
+
+| Modality | Router exposure share | Exact Shapley absolute share |
+|---|---:|---:|
+| text | 0.370 | 0.876 |
+| audio | 0.304 | 0.050 |
+| vision | 0.326 | 0.075 |
+
+Router–Shapley Spearman: 1.0; top modality agreement: True.
+Router values describe mixture routing. The counterfactual scores below test whether that routing signal tracks model behavior.
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 2–4 (relative progress 0.040–0.100), estimated clip interval 0.12–0.31s** — router_activity_not_signed_contribution; evidence: it
+- **text bins 46–46 (relative progress 0.920–0.940), estimated clip interval 2.86–2.93s** — router_activity_not_signed_contribution; evidence: movie
+- **audio bins 1–5 (relative progress 0.020–0.120), estimated clip interval 0.06–0.37s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 1–7; review the linked source clip at the estimated relative span
+- **vision bins 27–30 (relative progress 0.540–0.620), estimated clip interval 1.68–1.93s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 23–27; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_16_vision_1.jpg)
+- **vision bins 15–15 (relative progress 0.300–0.320), estimated clip interval 0.93–1.00s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 13–14; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_16_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E1_MoFE_Router/16.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.2071 / +1.2711 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.5664 / +0.1907. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +1.1494.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+It's a terrible, this is a terrible movie
diff --git a/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/17.md b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/17.md
new file mode 100644
index 0000000..2871ec9
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/17.md
@@ -0,0 +1,50 @@
+# Q3 explanation card — E1_MoFE_Router — 17
+
+## Prediction
+
+- Polarity: **positive**
+- Intensity: +0.928
+- Predicted-class confidence: 0.884
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/17.mp4
+
+## Router profile (intrinsic routing signal, not prediction contribution)
+
+| Modality | Router exposure share | Exact Shapley absolute share |
+|---|---:|---:|
+| text | 0.360 | 0.721 |
+| audio | 0.289 | 0.079 |
+| vision | 0.351 | 0.200 |
+
+Router–Shapley Spearman: 1.0; top modality agreement: True.
+Router values describe mixture routing. The counterfactual scores below test whether that routing signal tracks model behavior.
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 10–11 (relative progress 0.200–0.240), estimated clip interval 1.93–2.32s** — router_activity_not_signed_contribution; evidence: concepts to
+- **text bins 48–49 (relative progress 0.960–1.000), estimated clip interval 9.28–9.67s** — router_activity_not_signed_contribution; evidence: .
+- **audio bins 37–38 (relative progress 0.740–0.780), estimated clip interval 7.15–7.54s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 141–148; review the linked source clip at the estimated relative span
+- **audio bins 2–2 (relative progress 0.040–0.060), estimated clip interval 0.39–0.58s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 7–11; review the linked source clip at the estimated relative span
+- **vision bins 46–47 (relative progress 0.920–0.960), estimated clip interval 8.89–9.28s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 131–137; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_17_vision_1.jpg)
+- **vision bins 17–18 (relative progress 0.340–0.380), estimated clip interval 3.29–3.67s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 48–54; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_17_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E1_MoFE_Router/17.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.3064 / +1.3990 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.5635 / -0.0240. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +1.2303.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+Applying these four design concepts to your presentations is simple, easy and will make people think you turned into a design guru.
diff --git a/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/18.md b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/18.md
new file mode 100644
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--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/18.md
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+# Q3 explanation card — E1_MoFE_Router — 18
+
+## Prediction
+
+- Polarity: **negative**
+- Intensity: -0.171
+- Predicted-class confidence: 0.490
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/18.mp4
+
+## Router profile (intrinsic routing signal, not prediction contribution)
+
+| Modality | Router exposure share | Exact Shapley absolute share |
+|---|---:|---:|
+| text | 0.359 | 0.380 |
+| audio | 0.291 | 0.178 |
+| vision | 0.350 | 0.441 |
+
+Router–Shapley Spearman: 0.5; top modality agreement: False.
+Router values describe mixture routing. The counterfactual scores below test whether that routing signal tracks model behavior.
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 38–38 (relative progress 0.760–0.780), estimated clip interval 14.29–14.67s** — router_activity_not_signed_contribution; evidence: fishery
+- **text bins 7–7 (relative progress 0.140–0.160), estimated clip interval 2.63–3.01s** — router_activity_not_signed_contribution; evidence: first
+- **audio bins 38–38 (relative progress 0.760–0.780), estimated clip interval 14.29–14.67s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 282–290; review the linked source clip at the estimated relative span
+- **audio bins 4–4 (relative progress 0.080–0.100), estimated clip interval 1.50–1.88s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 29–37; review the linked source clip at the estimated relative span
+- **vision bins 10–11 (relative progress 0.200–0.240), estimated clip interval 3.76–4.51s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 56–67; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_18_vision_1.jpg)
+- **vision bins 2–2 (relative progress 0.040–0.060), estimated clip interval 0.75–1.13s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 11–16; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_18_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E1_MoFE_Router/18.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: -0.1406 / -0.3074 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +1.0792 / +0.8558. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: -0.0964.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+-And in Denmark - the first Baltic Cod fishery has been MSC – certified -Meanwhile, the Faeroese Mackerel Fishery has been denied MSC certification based on the fact that the fishery has failed to reach an agreement on mackerel quotas with Norway and the European Union.
diff --git a/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/19.md b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/19.md
new file mode 100644
index 0000000..497f9e9
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/19.md
@@ -0,0 +1,50 @@
+# Q3 explanation card — E1_MoFE_Router — 19
+
+## Prediction
+
+- Polarity: **positive**
+- Intensity: +0.047
+- Predicted-class confidence: 0.474
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/19.mp4
+
+## Router profile (intrinsic routing signal, not prediction contribution)
+
+| Modality | Router exposure share | Exact Shapley absolute share |
+|---|---:|---:|
+| text | 0.358 | 0.080 |
+| audio | 0.291 | 0.053 |
+| vision | 0.351 | 0.867 |
+
+Router–Shapley Spearman: 0.5; top modality agreement: False.
+Router values describe mixture routing. The counterfactual scores below test whether that routing signal tracks model behavior.
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 45–46 (relative progress 0.900–0.940), estimated clip interval 12.31–12.86s** — router_activity_not_signed_contribution; evidence: part, people
+- **text bins 30–30 (relative progress 0.600–0.620), estimated clip interval 8.21–8.48s** — router_activity_not_signed_contribution; evidence: fingers and
+- **audio bins 45–46 (relative progress 0.900–0.940), estimated clip interval 12.31–12.86s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 244–255; review the linked source clip at the estimated relative span
+- **audio bins 30–31 (relative progress 0.600–0.640), estimated clip interval 8.21–8.76s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 163–174; review the linked source clip at the estimated relative span
+- **vision bins 7–9 (relative progress 0.140–0.200), estimated clip interval 1.92–2.74s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 28–40; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_19_vision_1.jpg)
+- **vision bins 20–20 (relative progress 0.400–0.420), estimated clip interval 5.47–5.75s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 81–85; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_19_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E1_MoFE_Router/19.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.2485 / -0.0569 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.0621 / +0.5542. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +0.1364.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+People are surprisingly forgiving brands when they own up to mistakes, and unfortunately some haters out there love to point fingers and jump all over imperfections, but for the most part, people understand
diff --git a/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/20.md b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/20.md
new file mode 100644
index 0000000..bea76c7
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E1_MoFE_Router/20.md
@@ -0,0 +1,50 @@
+# Q3 explanation card — E1_MoFE_Router — 20
+
+## Prediction
+
+- Polarity: **positive**
+- Intensity: +0.760
+- Predicted-class confidence: 0.835
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/20.mp4
+
+## Router profile (intrinsic routing signal, not prediction contribution)
+
+| Modality | Router exposure share | Exact Shapley absolute share |
+|---|---:|---:|
+| text | 0.359 | 0.936 |
+| audio | 0.291 | 0.025 |
+| vision | 0.350 | 0.039 |
+
+Router–Shapley Spearman: 1.0; top modality agreement: True.
+Router values describe mixture routing. The counterfactual scores below test whether that routing signal tracks model behavior.
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 15–16 (relative progress 0.300–0.340), estimated clip interval 3.29–3.72s** — router_activity_not_signed_contribution; evidence: this video for
+- **text bins 33–34 (relative progress 0.660–0.700), estimated clip interval 7.23–7.67s** — router_activity_not_signed_contribution; evidence: market video (
+- **audio bins 33–35 (relative progress 0.660–0.720), estimated clip interval 7.23–7.89s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 142–155; review the linked source clip at the estimated relative span
+- **audio bins 16–16 (relative progress 0.320–0.340), estimated clip interval 3.50–3.72s** — router_activity_not_signed_contribution; evidence: unaligned audio feature rows 69–73; review the linked source clip at the estimated relative span
+- **vision bins 7–9 (relative progress 0.140–0.200), estimated clip interval 1.53–2.19s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 22–32; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_20_vision_1.jpg)
+- **vision bins 5–5 (relative progress 0.100–0.120), estimated clip interval 1.10–1.31s** — router_activity_not_signed_contribution; evidence: unaligned vision feature rows 16–19; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E1_MoFE_Router_20_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E1_MoFE_Router/20.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.5186 / +1.6799 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.5129 / +0.0639. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +1.3739.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+And of course, click in the link of the description of this video for more, and we'll have more live updates and a stock market video (wrap-up) at the end of the day today.
diff --git a/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/01.md b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/01.md
new file mode 100644
index 0000000..4862e2c
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/01.md
@@ -0,0 +1,58 @@
+# Q3 explanation card — E2_MoFE_Shapley — 01
+
+## Prediction
+
+- Polarity: **neutral**
+- Intensity: +0.251
+- Predicted-class confidence: 0.477
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/01.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +0.9563 | 0.642 | +0.6618 | 0.603 |
+| audio | -0.2072 | 0.139 | +0.1829 | 0.167 |
+| vision | -0.3270 | 0.219 | -0.2536 | 0.231 |
+
+Shapley completeness residuals: class -5.55e-17, intensity 0.00e+00.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | +0.1493 | -0.1375 |
+| Text + Vision | +0.1915 | +0.2039 |
+| Audio + Vision | +0.0868 | +0.0537 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 15–18 (relative progress 0.300–0.380), estimated clip interval 2.70–3.42s** — supports_predicted_class; evidence: the timing belt
+- **text bins 32–32 (relative progress 0.640–0.660), estimated clip interval 5.76–5.94s** — supports_predicted_class; evidence: new
+- **audio bins 1–2 (relative progress 0.020–0.060), estimated clip interval 0.18–0.54s** — opposes_predicted_class; evidence: unaligned audio feature rows 3–10; review the linked source clip at the estimated relative span
+- **audio bins 16–17 (relative progress 0.320–0.360), estimated clip interval 2.88–3.24s** — opposes_predicted_class; evidence: unaligned audio feature rows 57–64; review the linked source clip at the estimated relative span
+- **vision bins 5–8 (relative progress 0.100–0.180), estimated clip interval 0.90–1.62s** — opposes_predicted_class; evidence: unaligned vision feature rows 13–24; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_01_vision_1.jpg)
+- **vision bins 46–46 (relative progress 0.920–0.940), estimated clip interval 8.28–8.46s** — opposes_predicted_class; evidence: unaligned vision feature rows 124–126; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_01_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E2_MoFE_Shapley/01.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.4212 / +1.1640 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.0226 / -0.0818. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +0.8752.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+Replacing these wear components when replacing the timing belt is essential to ensuring the new belt performs to its mileage requirements
diff --git a/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/02.md b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/02.md
new file mode 100644
index 0000000..7f4d78c
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/02.md
@@ -0,0 +1,58 @@
+# Q3 explanation card — E2_MoFE_Shapley — 02
+
+## Prediction
+
+- Polarity: **neutral**
+- Intensity: +0.110
+- Predicted-class confidence: 0.462
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/02.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +0.6762 | 0.723 | +0.8219 | 0.671 |
+| audio | -0.0516 | 0.055 | +0.0157 | 0.013 |
+| vision | -0.2072 | 0.222 | -0.3872 | 0.316 |
+
+Shapley completeness residuals: class -5.55e-17, intensity 0.00e+00.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | +0.1744 | +0.0061 |
+| Text + Vision | +0.2674 | +0.1294 |
+| Audio + Vision | +0.0312 | -0.0598 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 26–28 (relative progress 0.520–0.580), estimated clip interval 1.82–2.03s** — supports_predicted_class; evidence: happiness - not
+- **text bins 39–39 (relative progress 0.780–0.800), estimated clip interval 2.72–2.79s** — supports_predicted_class; evidence: ’
+- **audio bins 31–33 (relative progress 0.620–0.680), estimated clip interval 2.17–2.38s** — supports_predicted_class; evidence: unaligned audio feature rows 40–44; review the linked source clip at the estimated relative span
+- **audio bins 35–36 (relative progress 0.700–0.740), estimated clip interval 2.45–2.58s** — supports_predicted_class; evidence: unaligned audio feature rows 46–48; review the linked source clip at the estimated relative span
+- **vision bins 40–43 (relative progress 0.800–0.880), estimated clip interval 2.79–3.07s** — opposes_predicted_class; evidence: unaligned vision feature rows 40–43; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_02_vision_1.jpg)
+- **vision bins 17–17 (relative progress 0.340–0.360), estimated clip interval 1.19–1.26s** — supports_predicted_class; evidence: unaligned vision feature rows 17–18; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_02_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E2_MoFE_Shapley/02.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.3460 / +1.0726 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.3472 / +0.2407. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +0.7905.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+We want to live by each other’s happiness - not by each other’s misery.
diff --git a/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/03.md b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/03.md
new file mode 100644
index 0000000..7c4048c
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/03.md
@@ -0,0 +1,58 @@
+# Q3 explanation card — E2_MoFE_Shapley — 03
+
+## Prediction
+
+- Polarity: **negative**
+- Intensity: -0.610
+- Predicted-class confidence: 0.688
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/03.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +1.2257 | 0.723 | -0.6208 | 0.639 |
+| audio | +0.0185 | 0.011 | +0.0243 | 0.025 |
+| vision | -0.4508 | 0.266 | +0.3268 | 0.336 |
+
+Shapley completeness residuals: class -1.11e-16, intensity 0.00e+00.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | -0.1065 | +0.1757 |
+| Text + Vision | +0.5189 | -0.3861 |
+| Audio + Vision | +0.1038 | -0.1471 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 44–45 (relative progress 0.880–0.920), estimated clip interval 8.50–8.89s** — supports_predicted_class; evidence: yield into account
+- **text bins 35–36 (relative progress 0.700–0.740), estimated clip interval 6.76–7.15s** — supports_predicted_class; evidence: into account
+- **audio bins 7–10 (relative progress 0.140–0.220), estimated clip interval 1.35–2.13s** — opposes_predicted_class; evidence: unaligned audio feature rows 26–42; review the linked source clip at the estimated relative span
+- **audio bins 3–3 (relative progress 0.060–0.080), estimated clip interval 0.58–0.77s** — opposes_predicted_class; evidence: unaligned audio feature rows 11–15; review the linked source clip at the estimated relative span
+- **vision bins 42–44 (relative progress 0.840–0.900), estimated clip interval 8.12–8.70s** — supports_predicted_class; evidence: unaligned vision feature rows 120–129; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_03_vision_1.jpg)
+- **vision bins 7–8 (relative progress 0.140–0.180), estimated clip interval 1.35–1.74s** — supports_predicted_class; evidence: unaligned vision feature rows 20–25; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_03_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E2_MoFE_Shapley/03.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.7735 / +1.4775 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: -0.6596 / -0.0313. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +1.1804.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+There's one lender at the moment which I think is just Bankwest who don't take rental income into account, they take rental yield into account.
diff --git a/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/04.md b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/04.md
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+# Q3 explanation card — E2_MoFE_Shapley — 04
+
+## Prediction
+
+- Polarity: **negative**
+- Intensity: -0.942
+- Predicted-class confidence: 0.784
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/04.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +1.6040 | 0.789 | -1.1000 | 0.688 |
+| audio | -0.3985 | 0.196 | +0.4059 | 0.254 |
+| vision | -0.0300 | 0.015 | +0.0925 | 0.058 |
+
+Shapley completeness residuals: class -2.22e-16, intensity 0.00e+00.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | +0.1932 | -0.1284 |
+| Text + Vision | +0.2459 | -0.1719 |
+| Audio + Vision | +0.2132 | -0.2422 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 43–45 (relative progress 0.860–0.920), estimated clip interval 6.01–6.43s** — supports_predicted_class; evidence: absolutely not
+- **text bins 6–7 (relative progress 0.120–0.160), estimated clip interval 0.84–1.12s** — supports_predicted_class; evidence: blow it
+- **audio bins 39–42 (relative progress 0.780–0.860), estimated clip interval 5.46–6.01s** — opposes_predicted_class; evidence: unaligned audio feature rows 106–117; review the linked source clip at the estimated relative span
+- **audio bins 12–12 (relative progress 0.240–0.260), estimated clip interval 1.68–1.82s** — supports_predicted_class; evidence: unaligned audio feature rows 32–35; review the linked source clip at the estimated relative span
+- **vision bins 39–41 (relative progress 0.780–0.840), estimated clip interval 5.46–5.87s** — supports_predicted_class; evidence: unaligned vision feature rows 80–86; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_04_vision_1.jpg)
+- **vision bins 31–32 (relative progress 0.620–0.660), estimated clip interval 4.34–4.62s** — supports_predicted_class; evidence: unaligned vision feature rows 63–67; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_04_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E2_MoFE_Shapley/04.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.7235 / +2.1628 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: -0.3664 / -0.2742. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +1.6747.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+If I blow it at the team exercise, should I kiss my chances of cheering "GO BLUE" goodbye?] Absolutely not.
diff --git a/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/05.md b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/05.md
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+# Q3 explanation card — E2_MoFE_Shapley — 05
+
+## Prediction
+
+- Polarity: **positive**
+- Intensity: +0.871
+- Predicted-class confidence: 0.837
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/05.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +0.8392 | 0.456 | +0.6213 | 0.513 |
+| audio | +0.2036 | 0.111 | +0.0360 | 0.030 |
+| vision | +0.7976 | 0.433 | +0.5540 | 0.457 |
+
+Shapley completeness residuals: class -2.22e-16, intensity 0.00e+00.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | -0.1651 | -0.0478 |
+| Text + Vision | -0.4251 | -0.3215 |
+| Audio + Vision | -0.1776 | -0.0663 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 6–10 (relative progress 0.120–0.220), estimated clip interval 0.73–1.34s** — supports_predicted_class; evidence: , my
+- **audio bins 1–5 (relative progress 0.020–0.120), estimated clip interval 0.12–0.73s** — opposes_predicted_class; evidence: unaligned audio feature rows 2–14; review the linked source clip at the estimated relative span
+- **vision bins 43–47 (relative progress 0.860–0.960), estimated clip interval 5.25–5.86s** — supports_predicted_class; evidence: unaligned vision feature rows 77–86; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_05_vision_1.jpg)
+
+![Local evidence profile](../../evidence_profiles/E2_MoFE_Shapley/05.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.3077 / +0.3107 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.2027 / +0.2257. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +0.4245.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+Hi, my name is Chloe, video marketer for Red Wagon Marketing.
diff --git a/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/06.md b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/06.md
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+# Q3 explanation card — E2_MoFE_Shapley — 06
+
+## Prediction
+
+- Polarity: **positive**
+- Intensity: +0.790
+- Predicted-class confidence: 0.805
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/06.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +1.8127 | 0.801 | +1.1609 | 0.800 |
+| audio | -0.2741 | 0.121 | -0.1604 | 0.111 |
+| vision | +0.1757 | 0.078 | +0.1295 | 0.089 |
+
+Shapley completeness residuals: class 0.00e+00, intensity -2.22e-16.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | +0.0391 | +0.1084 |
+| Text + Vision | -0.1121 | -0.1039 |
+| Audio + Vision | -0.1589 | -0.2370 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 21–23 (relative progress 0.420–0.480), estimated clip interval 3.49–3.99s** — supports_predicted_class; evidence: to watch people
+- **text bins 7–8 (relative progress 0.140–0.180), estimated clip interval 1.16–1.49s** — supports_predicted_class; evidence: a fan
+- **audio bins 42–44 (relative progress 0.840–0.900), estimated clip interval 6.98–7.47s** — opposes_predicted_class; evidence: unaligned audio feature rows 136–146; review the linked source clip at the estimated relative span
+- **audio bins 14–15 (relative progress 0.280–0.320), estimated clip interval 2.33–2.66s** — opposes_predicted_class; evidence: unaligned audio feature rows 45–52; review the linked source clip at the estimated relative span
+- **vision bins 40–44 (relative progress 0.800–0.900), estimated clip interval 6.64–7.47s** — opposes_predicted_class; evidence: unaligned vision feature rows 98–110; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_06_vision_1.jpg)
+
+![Local evidence profile](../../evidence_profiles/E2_MoFE_Shapley/06.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.6819 / +1.6836 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: -0.0933 / -0.2532. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +1.2527.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+If you're a fan of dancing in that sense, just like to watch people dance, see impressive dance moves then you might want to check out this movie solely for that
diff --git a/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/07.md b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/07.md
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+# Q3 explanation card — E2_MoFE_Shapley — 07
+
+## Prediction
+
+- Polarity: **positive**
+- Intensity: +0.580
+- Predicted-class confidence: 0.734
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/07.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +1.2243 | 0.838 | +1.0975 | 0.705 |
+| audio | +0.2240 | 0.153 | +0.1413 | 0.091 |
+| vision | +0.0135 | 0.009 | -0.3190 | 0.205 |
+
+Shapley completeness residuals: class -2.22e-16, intensity 0.00e+00.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | -0.2760 | -0.1914 |
+| Text + Vision | +0.0270 | +0.2232 |
+| Audio + Vision | -0.1128 | -0.0191 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 22–26 (relative progress 0.440–0.540), estimated clip interval 9.39–11.53s** — supports_predicted_class; evidence: discussed the topics of growing
+- **audio bins 25–26 (relative progress 0.500–0.540), estimated clip interval 10.67–11.53s** — opposes_predicted_class; evidence: unaligned audio feature rows 212–229; review the linked source clip at the estimated relative span
+- **audio bins 30–31 (relative progress 0.600–0.640), estimated clip interval 12.81–13.66s** — opposes_predicted_class; evidence: unaligned audio feature rows 255–271; review the linked source clip at the estimated relative span
+- **vision bins 38–41 (relative progress 0.760–0.840), estimated clip interval 16.22–17.93s** — opposes_predicted_class; evidence: unaligned vision feature rows 242–267; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_07_vision_1.jpg)
+- **vision bins 33–33 (relative progress 0.660–0.680), estimated clip interval 14.09–14.52s** — opposes_predicted_class; evidence: unaligned vision feature rows 210–216; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_07_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E2_MoFE_Shapley/07.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.4303 / +0.7252 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: -0.3333 / +0.0765. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +0.6468.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+As Linn’s associate editor Michael Baadke reports in our November 28 issue, attendees “enthusiastically discussed the topics of growing the hobby, the future of stamp shows, and dealers and philatelic partnerships, along with ways the leading organizations involved in the stamp hobby can work together to make it succeed and grow
diff --git a/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/08.md b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/08.md
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--- /dev/null
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@@ -0,0 +1,58 @@
+# Q3 explanation card — E2_MoFE_Shapley — 08
+
+## Prediction
+
+- Polarity: **positive**
+- Intensity: +0.712
+- Predicted-class confidence: 0.812
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/08.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +1.5797 | 0.888 | +1.1787 | 0.758 |
+| audio | +0.1601 | 0.090 | +0.1247 | 0.080 |
+| vision | +0.0394 | 0.022 | -0.2514 | 0.162 |
+
+Shapley completeness residuals: class 0.00e+00, intensity 0.00e+00.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | -0.3202 | -0.2654 |
+| Text + Vision | +0.0109 | +0.2232 |
+| Audio + Vision | -0.1553 | -0.0298 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 10–13 (relative progress 0.200–0.280), estimated clip interval 1.02–1.42s** — supports_predicted_class; evidence: brings us
+- **text bins 17–17 (relative progress 0.340–0.360), estimated clip interval 1.73–1.83s** — supports_predicted_class; evidence: to
+- **audio bins 46–48 (relative progress 0.920–0.980), estimated clip interval 4.68–4.98s** — opposes_predicted_class; evidence: unaligned audio feature rows 90–96; review the linked source clip at the estimated relative span
+- **audio bins 42–43 (relative progress 0.840–0.880), estimated clip interval 4.27–4.48s** — opposes_predicted_class; evidence: unaligned audio feature rows 82–86; review the linked source clip at the estimated relative span
+- **vision bins 23–26 (relative progress 0.460–0.540), estimated clip interval 2.34–2.75s** — opposes_predicted_class; evidence: unaligned vision feature rows 34–39; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_08_vision_1.jpg)
+- **vision bins 43–43 (relative progress 0.860–0.880), estimated clip interval 4.37–4.48s** — opposes_predicted_class; evidence: unaligned vision feature rows 63–65; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_08_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E2_MoFE_Shapley/08.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.4306 / +0.8107 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: -0.3601 / +0.0323. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +0.8594.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+That brings us to tonight, the Universal Design Grand Challenge
diff --git a/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/09.md b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/09.md
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+++ b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/09.md
@@ -0,0 +1,58 @@
+# Q3 explanation card — E2_MoFE_Shapley — 09
+
+## Prediction
+
+- Polarity: **negative**
+- Intensity: -1.848
+- Predicted-class confidence: 0.918
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/09.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +1.9453 | 0.940 | -1.5164 | 0.981 |
+| audio | +0.1079 | 0.052 | -0.0104 | 0.007 |
+| vision | -0.0172 | 0.008 | +0.0193 | 0.012 |
+
+Shapley completeness residuals: class 0.00e+00, intensity 2.22e-16.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | -0.0704 | +0.0883 |
+| Text + Vision | +0.1085 | -0.0059 |
+| Audio + Vision | +0.0982 | -0.1718 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 27–30 (relative progress 0.540–0.620), estimated clip interval 2.72–3.13s** — supports_predicted_class; evidence: movie at all
+- **text bins 35–35 (relative progress 0.700–0.720), estimated clip interval 3.53–3.63s** — supports_predicted_class; evidence: , i
+- **audio bins 47–49 (relative progress 0.940–1.000), estimated clip interval 4.74–5.04s** — supports_predicted_class; evidence: unaligned audio feature rows 93–98; review the linked source clip at the estimated relative span
+- **audio bins 2–3 (relative progress 0.040–0.080), estimated clip interval 0.20–0.40s** — supports_predicted_class; evidence: unaligned audio feature rows 3–7; review the linked source clip at the estimated relative span
+- **vision bins 11–13 (relative progress 0.220–0.280), estimated clip interval 1.11–1.41s** — supports_predicted_class; evidence: unaligned vision feature rows 16–20; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_09_vision_1.jpg)
+- **vision bins 20–21 (relative progress 0.400–0.440), estimated clip interval 2.02–2.22s** — supports_predicted_class; evidence: unaligned vision feature rows 30–32; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_09_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E2_MoFE_Shapley/09.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.4802 / +1.6975 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.3075 / +0.1968. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +1.4469.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+(uhh) I did not like this movie at all, I would not recommend it
diff --git a/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/10.md b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/10.md
new file mode 100644
index 0000000..7d388ea
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/10.md
@@ -0,0 +1,56 @@
+# Q3 explanation card — E2_MoFE_Shapley — 10
+
+## Prediction
+
+- Polarity: **negative**
+- Intensity: -1.260
+- Predicted-class confidence: 0.862
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/10.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +1.7953 | 0.665 | -1.3590 | 0.640 |
+| audio | -0.5743 | 0.213 | +0.6022 | 0.284 |
+| vision | +0.3315 | 0.123 | -0.1628 | 0.077 |
+
+Shapley completeness residuals: class -2.22e-16, intensity 0.00e+00.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | +0.3703 | -0.1735 |
+| Text + Vision | -0.0413 | -0.0734 |
+| Audio + Vision | +0.3672 | -0.3802 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 7–9 (relative progress 0.140–0.200), estimated clip interval 1.39–1.99s** — supports_predicted_class; evidence: and you know
+- **text bins 19–19 (relative progress 0.380–0.400), estimated clip interval 3.78–3.98s** — supports_predicted_class; evidence: see
+- **audio bins 4–6 (relative progress 0.080–0.140), estimated clip interval 0.80–1.39s** — supports_predicted_class; evidence: unaligned audio feature rows 15–27; review the linked source clip at the estimated relative span
+- **audio bins 16–16 (relative progress 0.320–0.340), estimated clip interval 3.18–3.38s** — opposes_predicted_class; evidence: unaligned audio feature rows 62–66; review the linked source clip at the estimated relative span
+- **vision bins 22–26 (relative progress 0.440–0.540), estimated clip interval 4.37–5.37s** — supports_predicted_class; evidence: unaligned vision feature rows 64–79; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_10_vision_1.jpg)
+
+![Local evidence profile](../../evidence_profiles/E2_MoFE_Shapley/10.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.5884 / +2.0317 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.1454 / +0.0604. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +1.9366.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+(umm) And you know I really do like to see fluffy chick flicks sometimes so I'm not against that but this one was pretty terrible
diff --git a/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/11.md b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/11.md
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+# Q3 explanation card — E2_MoFE_Shapley — 11
+
+## Prediction
+
+- Polarity: **negative**
+- Intensity: -0.492
+- Predicted-class confidence: 0.577
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/11.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +0.7143 | 0.543 | -0.2606 | 0.310 |
+| audio | +0.1745 | 0.133 | -0.2353 | 0.280 |
+| vision | -0.4272 | 0.325 | +0.3442 | 0.410 |
+
+Shapley completeness residuals: class -1.11e-16, intensity 0.00e+00.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | +0.0550 | +0.1165 |
+| Text + Vision | -0.0932 | +0.0589 |
+| Audio + Vision | +0.3224 | -0.2273 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 8–12 (relative progress 0.160–0.260), estimated clip interval 0.93–1.51s** — supports_predicted_class; evidence: would be ashamed
+- **audio bins 2–4 (relative progress 0.040–0.100), estimated clip interval 0.23–0.58s** — supports_predicted_class; evidence: unaligned audio feature rows 4–11; review the linked source clip at the estimated relative span
+- **audio bins 33–33 (relative progress 0.660–0.680), estimated clip interval 3.83–3.95s** — supports_predicted_class; evidence: unaligned audio feature rows 75–77; review the linked source clip at the estimated relative span
+- **vision bins 47–48 (relative progress 0.940–0.980), estimated clip interval 5.46–5.69s** — opposes_predicted_class; evidence: unaligned vision feature rows 80–84; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_11_vision_1.jpg)
+- **vision bins 4–4 (relative progress 0.080–0.100), estimated clip interval 0.46–0.58s** — opposes_predicted_class; evidence: unaligned vision feature rows 6–8; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_11_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E2_MoFE_Shapley/11.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.6028 / +0.5872 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: -1.1319 / -0.2942. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +0.5614.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+I would be ashamed to have made this film if I was a director
diff --git a/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/12.md b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/12.md
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--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/12.md
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+# Q3 explanation card — E2_MoFE_Shapley — 12
+
+## Prediction
+
+- Polarity: **negative**
+- Intensity: -1.088
+- Predicted-class confidence: 0.814
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/12.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +1.1328 | 0.518 | -0.5134 | 0.367 |
+| audio | -0.4324 | 0.198 | +0.3258 | 0.233 |
+| vision | +0.6203 | 0.284 | -0.5603 | 0.400 |
+
+Shapley completeness residuals: class 0.00e+00, intensity 0.00e+00.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | +0.0606 | +0.0851 |
+| Text + Vision | -0.0521 | +0.0239 |
+| Audio + Vision | +0.1899 | -0.0598 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 43–45 (relative progress 0.860–0.920), estimated clip interval 11.19–11.97s** — supports_predicted_class; evidence: credit scores is very
+- **text bins 9–9 (relative progress 0.180–0.200), estimated clip interval 2.34–2.60s** — supports_predicted_class; evidence: had good
+- **audio bins 2–5 (relative progress 0.040–0.120), estimated clip interval 0.52–1.56s** — opposes_predicted_class; evidence: unaligned audio feature rows 10–30; review the linked source clip at the estimated relative span
+- **audio bins 10–10 (relative progress 0.200–0.220), estimated clip interval 2.60–2.86s** — opposes_predicted_class; evidence: unaligned audio feature rows 51–56; review the linked source clip at the estimated relative span
+- **vision bins 10–14 (relative progress 0.200–0.300), estimated clip interval 2.60–3.90s** — supports_predicted_class; evidence: unaligned vision feature rows 38–57; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_12_vision_1.jpg)
+
+![Local evidence profile](../../evidence_profiles/E2_MoFE_Shapley/12.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.3935 / +1.1091 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: -0.0087 / -0.1549. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +1.1331.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+Or worse, an individual previously had good credit, but usually by no fault of their own, or perhaps by fault of their own, they have let their credit sag, and credit scores is very low.
diff --git a/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/13.md b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/13.md
new file mode 100644
index 0000000..caeea3b
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/13.md
@@ -0,0 +1,58 @@
+# Q3 explanation card — E2_MoFE_Shapley — 13
+
+## Prediction
+
+- Polarity: **positive**
+- Intensity: +0.421
+- Predicted-class confidence: 0.525
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/13.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +0.5548 | 0.515 | +0.6172 | 0.544 |
+| audio | -0.1353 | 0.126 | -0.1868 | 0.165 |
+| vision | +0.3869 | 0.359 | +0.3305 | 0.291 |
+
+Shapley completeness residuals: class -1.11e-16, intensity 0.00e+00.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | -0.1396 | +0.1153 |
+| Text + Vision | -0.1662 | -0.1338 |
+| Audio + Vision | -0.2975 | -0.3222 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 19–21 (relative progress 0.380–0.440), estimated clip interval 3.30–3.82s** — supports_predicted_class; evidence: from that data
+- **text bins 9–10 (relative progress 0.180–0.220), estimated clip interval 1.56–1.91s** — opposes_predicted_class; evidence: could take a
+- **audio bins 7–9 (relative progress 0.140–0.200), estimated clip interval 1.22–1.74s** — opposes_predicted_class; evidence: unaligned audio feature rows 24–34; review the linked source clip at the estimated relative span
+- **audio bins 26–27 (relative progress 0.520–0.560), estimated clip interval 4.51–4.86s** — opposes_predicted_class; evidence: unaligned audio feature rows 89–96; review the linked source clip at the estimated relative span
+- **vision bins 19–21 (relative progress 0.380–0.440), estimated clip interval 3.30–3.82s** — supports_predicted_class; evidence: unaligned vision feature rows 6–7; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_13_vision_1.jpg)
+- **vision bins 5–5 (relative progress 0.100–0.120), estimated clip interval 0.87–1.04s** — supports_predicted_class; evidence: unaligned vision feature rows 2–2; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_13_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E2_MoFE_Shapley/13.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: -0.1583 / -0.5077 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.4472 / +0.3904. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: -0.3368.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+For example, I could take a set of data and from that data, I can find a relationship between any two of the given factors or more.
diff --git a/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/14.md b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/14.md
new file mode 100644
index 0000000..49a6784
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/14.md
@@ -0,0 +1,58 @@
+# Q3 explanation card — E2_MoFE_Shapley — 14
+
+## Prediction
+
+- Polarity: **positive**
+- Intensity: +0.526
+- Predicted-class confidence: 0.582
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/14.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | -0.1328 | 0.115 | +0.2202 | 0.254 |
+| audio | +0.2039 | 0.177 | +0.1511 | 0.174 |
+| vision | +0.8151 | 0.708 | +0.4951 | 0.571 |
+
+Shapley completeness residuals: class 0.00e+00, intensity 0.00e+00.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | -0.2578 | -0.0941 |
+| Text + Vision | -0.2702 | -0.2846 |
+| Audio + Vision | -0.3436 | -0.2660 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 29–30 (relative progress 0.580–0.620), estimated clip interval 6.00–6.42s** — opposes_predicted_class; evidence: uniform final
+- **text bins 6–7 (relative progress 0.120–0.160), estimated clip interval 1.24–1.66s** — opposes_predicted_class; evidence: co -
+- **audio bins 32–35 (relative progress 0.640–0.720), estimated clip interval 6.62–7.45s** — opposes_predicted_class; evidence: unaligned audio feature rows 130–146; review the linked source clip at the estimated relative span
+- **audio bins 15–15 (relative progress 0.300–0.320), estimated clip interval 3.11–3.31s** — opposes_predicted_class; evidence: unaligned audio feature rows 61–65; review the linked source clip at the estimated relative span
+- **vision bins 0–2 (relative progress 0.000–0.060), estimated clip interval 0.00–0.62s** — supports_predicted_class; evidence: unaligned vision feature rows 0–9; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_14_vision_1.jpg)
+- **vision bins 47–48 (relative progress 0.940–0.980), estimated clip interval 9.73–10.14s** — supports_predicted_class; evidence: unaligned vision feature rows 143–149; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_14_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E2_MoFE_Shapley/14.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: -0.3806 / -0.4348 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.8105 / +0.3206. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: -0.3162.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+He is the co-founder of Rossen and Vettese Limited and the former Executive Director of Uniform Final Examination (UFE) courses at Toronto's York University.
diff --git a/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/15.md b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/15.md
new file mode 100644
index 0000000..d83df11
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/15.md
@@ -0,0 +1,56 @@
+# Q3 explanation card — E2_MoFE_Shapley — 15
+
+## Prediction
+
+- Polarity: **positive**
+- Intensity: +1.053
+- Predicted-class confidence: 0.893
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/15.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +1.0820 | 0.496 | +0.7277 | 0.522 |
+| audio | +0.5320 | 0.244 | +0.2927 | 0.210 |
+| vision | +0.5664 | 0.260 | +0.3724 | 0.267 |
+
+Shapley completeness residuals: class 0.00e+00, intensity 0.00e+00.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | -0.3240 | -0.1826 |
+| Text + Vision | -0.2097 | -0.1495 |
+| Audio + Vision | -0.3615 | -0.3010 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 40–42 (relative progress 0.800–0.860), estimated clip interval 5.87–6.31s** — supports_predicted_class; evidence: power to
+- **text bins 11–11 (relative progress 0.220–0.240), estimated clip interval 1.61–1.76s** — opposes_predicted_class; evidence: poverty
+- **audio bins 47–49 (relative progress 0.940–1.000), estimated clip interval 6.89–7.33s** — supports_predicted_class; evidence: unaligned audio feature rows 136–144; review the linked source clip at the estimated relative span
+- **audio bins 13–14 (relative progress 0.260–0.300), estimated clip interval 1.91–2.20s** — supports_predicted_class; evidence: unaligned audio feature rows 37–43; review the linked source clip at the estimated relative span
+- **vision bins 11–15 (relative progress 0.220–0.320), estimated clip interval 1.61–2.35s** — supports_predicted_class; evidence: unaligned vision feature rows 24–35; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_15_vision_1.jpg)
+
+![Local evidence profile](../../evidence_profiles/E2_MoFE_Shapley/15.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.1774 / +0.6323 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.7456 / +0.2484. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +0.6086.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+However, despite their poverty, the family prioritize education because they believed in its power to transform lives
diff --git a/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/16.md b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/16.md
new file mode 100644
index 0000000..a7d44ad
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/16.md
@@ -0,0 +1,57 @@
+# Q3 explanation card — E2_MoFE_Shapley — 16
+
+## Prediction
+
+- Polarity: **negative**
+- Intensity: -1.836
+- Predicted-class confidence: 0.914
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/16.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +1.7042 | 0.876 | -1.2273 | 0.821 |
+| audio | +0.0968 | 0.050 | -0.0746 | 0.050 |
+| vision | +0.1450 | 0.075 | -0.1934 | 0.129 |
+
+Shapley completeness residuals: class -4.44e-16, intensity 0.00e+00.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | -0.0912 | +0.0715 |
+| Text + Vision | +0.0089 | +0.0956 |
+| Audio + Vision | -0.0782 | +0.0493 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 43–44 (relative progress 0.860–0.900), estimated clip interval 2.68–2.80s** — supports_predicted_class; evidence: movie
+- **text bins 15–16 (relative progress 0.300–0.340), estimated clip interval 0.93–1.06s** — supports_predicted_class; evidence: s a
+- **audio bins 45–49 (relative progress 0.900–1.000), estimated clip interval 2.80–3.11s** — opposes_predicted_class; evidence: unaligned audio feature rows 54–59; review the linked source clip at the estimated relative span
+- **vision bins 45–46 (relative progress 0.900–0.940), estimated clip interval 2.80–2.93s** — supports_predicted_class; evidence: unaligned vision feature rows 39–41; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_16_vision_1.jpg)
+- **vision bins 31–32 (relative progress 0.620–0.660), estimated clip interval 1.93–2.06s** — supports_predicted_class; evidence: unaligned vision feature rows 27–29; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_16_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E2_MoFE_Shapley/16.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.4228 / +1.5463 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.3910 / +0.0861. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +1.2233.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+It's a terrible, this is a terrible movie
diff --git a/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/17.md b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/17.md
new file mode 100644
index 0000000..bc4d586
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/17.md
@@ -0,0 +1,58 @@
+# Q3 explanation card — E2_MoFE_Shapley — 17
+
+## Prediction
+
+- Polarity: **positive**
+- Intensity: +0.928
+- Predicted-class confidence: 0.884
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/17.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +1.8485 | 0.721 | +1.3263 | 0.732 |
+| audio | -0.2026 | 0.079 | -0.2719 | 0.150 |
+| vision | +0.5129 | 0.200 | +0.2134 | 0.118 |
+
+Shapley completeness residuals: class 0.00e+00, intensity -2.22e-16.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | -0.0578 | +0.1400 |
+| Text + Vision | -0.2487 | -0.1171 |
+| Audio + Vision | -0.1361 | -0.1742 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 29–31 (relative progress 0.580–0.640), estimated clip interval 5.61–6.19s** — supports_predicted_class; evidence: make people
+- **text bins 21–22 (relative progress 0.420–0.460), estimated clip interval 4.06–4.45s** — supports_predicted_class; evidence: simple,
+- **audio bins 46–48 (relative progress 0.920–0.980), estimated clip interval 8.89–9.47s** — opposes_predicted_class; evidence: unaligned audio feature rows 175–187; review the linked source clip at the estimated relative span
+- **audio bins 11–12 (relative progress 0.220–0.260), estimated clip interval 2.13–2.51s** — opposes_predicted_class; evidence: unaligned audio feature rows 42–49; review the linked source clip at the estimated relative span
+- **vision bins 30–32 (relative progress 0.600–0.660), estimated clip interval 5.80–6.38s** — supports_predicted_class; evidence: unaligned vision feature rows 85–94; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_17_vision_1.jpg)
+- **vision bins 4–5 (relative progress 0.080–0.120), estimated clip interval 0.77–1.16s** — supports_predicted_class; evidence: unaligned vision feature rows 11–17; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_17_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E2_MoFE_Shapley/17.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.5239 / +1.5241 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: -0.0036 / -0.1295. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +1.1456.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+Applying these four design concepts to your presentations is simple, easy and will make people think you turned into a design guru.
diff --git a/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/18.md b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/18.md
new file mode 100644
index 0000000..1f6f01c
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/18.md
@@ -0,0 +1,58 @@
+# Q3 explanation card — E2_MoFE_Shapley — 18
+
+## Prediction
+
+- Polarity: **negative**
+- Intensity: -0.171
+- Predicted-class confidence: 0.490
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/18.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | -0.3800 | 0.380 | +0.5236 | 0.558 |
+| audio | +0.1779 | 0.178 | +0.0299 | 0.032 |
+| vision | +0.4407 | 0.441 | -0.3844 | 0.410 |
+
+Shapley completeness residuals: class 0.00e+00, intensity 0.00e+00.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | -0.0407 | +0.1208 |
+| Text + Vision | +0.2032 | +0.0182 |
+| Audio + Vision | +0.0530 | -0.1900 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 3–6 (relative progress 0.060–0.140), estimated clip interval 1.13–2.63s** — opposes_predicted_class; evidence: in denmark - the
+- **text bins 15–15 (relative progress 0.300–0.320), estimated clip interval 5.64–6.02s** — opposes_predicted_class; evidence: – certified
+- **audio bins 7–10 (relative progress 0.140–0.220), estimated clip interval 2.63–4.14s** — supports_predicted_class; evidence: unaligned audio feature rows 52–81; review the linked source clip at the estimated relative span
+- **audio bins 23–23 (relative progress 0.460–0.480), estimated clip interval 8.65–9.03s** — opposes_predicted_class; evidence: unaligned audio feature rows 171–178; review the linked source clip at the estimated relative span
+- **vision bins 43–46 (relative progress 0.860–0.940), estimated clip interval 16.17–17.68s** — supports_predicted_class; evidence: unaligned vision feature rows 240–263; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_18_vision_1.jpg)
+- **vision bins 10–10 (relative progress 0.200–0.220), estimated clip interval 3.76–4.14s** — supports_predicted_class; evidence: unaligned vision feature rows 56–61; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_18_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E2_MoFE_Shapley/18.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: -0.4081 / +0.0710 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +1.4230 / +0.5937. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +0.0147.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+-And in Denmark - the first Baltic Cod fishery has been MSC – certified -Meanwhile, the Faeroese Mackerel Fishery has been denied MSC certification based on the fact that the fishery has failed to reach an agreement on mackerel quotas with Norway and the European Union.
diff --git a/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/19.md b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/19.md
new file mode 100644
index 0000000..b8b44d6
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/19.md
@@ -0,0 +1,56 @@
+# Q3 explanation card — E2_MoFE_Shapley — 19
+
+## Prediction
+
+- Polarity: **positive**
+- Intensity: +0.047
+- Predicted-class confidence: 0.474
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/19.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | -0.0595 | 0.080 | +0.1895 | 0.273 |
+| audio | -0.0395 | 0.053 | -0.1530 | 0.221 |
+| vision | +0.6449 | 0.867 | +0.3506 | 0.506 |
+
+Shapley completeness residuals: class -1.11e-16, intensity 0.00e+00.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | -0.0337 | +0.2002 |
+| Text + Vision | +0.0045 | -0.0336 |
+| Audio + Vision | -0.1129 | -0.1968 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 34–36 (relative progress 0.680–0.740), estimated clip interval 9.30–10.13s** — opposes_predicted_class; evidence: all over imperfections
+- **text bins 12–13 (relative progress 0.240–0.280), estimated clip interval 3.28–3.83s** — opposes_predicted_class; evidence: own up to
+- **audio bins 16–18 (relative progress 0.320–0.380), estimated clip interval 4.38–5.20s** — opposes_predicted_class; evidence: unaligned audio feature rows 87–103; review the linked source clip at the estimated relative span
+- **audio bins 0–1 (relative progress 0.000–0.040), estimated clip interval 0.00–0.55s** — opposes_predicted_class; evidence: unaligned audio feature rows 0–10; review the linked source clip at the estimated relative span
+- **vision bins 26–30 (relative progress 0.520–0.620), estimated clip interval 7.12–8.48s** — supports_predicted_class; evidence: unaligned vision feature rows 106–126; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_19_vision_1.jpg)
+
+![Local evidence profile](../../evidence_profiles/E2_MoFE_Shapley/19.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.0467 / +0.1815 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.2694 / +0.1343. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +0.1936.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+People are surprisingly forgiving brands when they own up to mistakes, and unfortunately some haters out there love to point fingers and jump all over imperfections, but for the most part, people understand
diff --git a/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/20.md b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/20.md
new file mode 100644
index 0000000..455333d
--- /dev/null
+++ b/final/output/q3/first_round/explanation_cards/E2_MoFE_Shapley/20.md
@@ -0,0 +1,58 @@
+# Q3 explanation card — E2_MoFE_Shapley — 20
+
+## Prediction
+
+- Polarity: **positive**
+- Intensity: +0.760
+- Predicted-class confidence: 0.835
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/20.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +1.8305 | 0.936 | +1.4182 | 0.756 |
+| audio | -0.0497 | 0.025 | -0.3878 | 0.207 |
+| vision | +0.0764 | 0.039 | +0.0693 | 0.037 |
+
+Shapley completeness residuals: class 0.00e+00, intensity 0.00e+00.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | +0.1485 | +0.4687 |
+| Text + Vision | -0.1556 | -0.1111 |
+| Audio + Vision | -0.1757 | -0.2182 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 11–13 (relative progress 0.220–0.280), estimated clip interval 2.41–3.07s** — supports_predicted_class; evidence: of the description of
+- **text bins 28–28 (relative progress 0.560–0.580), estimated clip interval 6.13–6.35s** — supports_predicted_class; evidence: updates and
+- **audio bins 33–34 (relative progress 0.660–0.700), estimated clip interval 7.23–7.67s** — opposes_predicted_class; evidence: unaligned audio feature rows 142–151; review the linked source clip at the estimated relative span
+- **audio bins 26–27 (relative progress 0.520–0.560), estimated clip interval 5.70–6.13s** — supports_predicted_class; evidence: unaligned audio feature rows 112–120; review the linked source clip at the estimated relative span
+- **vision bins 13–15 (relative progress 0.260–0.320), estimated clip interval 2.85–3.50s** — opposes_predicted_class; evidence: unaligned vision feature rows 42–51; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_20_vision_1.jpg)
+- **vision bins 34–35 (relative progress 0.680–0.720), estimated clip interval 7.45–7.89s** — opposes_predicted_class; evidence: unaligned vision feature rows 110–116; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_20_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E2_MoFE_Shapley/20.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.6271 / +1.7907 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: +0.1621 / -0.0744. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +1.4280.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+And of course, click in the link of the description of this video for more, and we'll have more live updates and a stock market video (wrap-up) at the end of the day today.
diff --git a/final/output/q3/first_round/faithfulness_by_sample.csv b/final/output/q3/first_round/faithfulness_by_sample.csv
new file mode 100644
index 0000000..22d0eb1
--- /dev/null
+++ b/final/output/q3/first_round/faithfulness_by_sample.csv
@@ -0,0 +1,61 @@
+observed_cells,ranking_cells,comprehensiveness_delete_0pct,deleted_cells_0pct,comprehensiveness_delete_10pct,deleted_cells_10pct,comprehensiveness_delete_20pct,deleted_cells_20pct,comprehensiveness_delete_30pct,deleted_cells_30pct,comprehensiveness_delete_50pct,deleted_cells_50pct,comprehensiveness_delete_70pct,deleted_cells_70pct,sufficiency_gap_retain_10pct,sufficiency_abs_gap_retain_10pct,retained_cells_10pct,sufficiency_gap_retain_20pct,sufficiency_abs_gap_retain_20pct,retained_cells_20pct,sufficiency_gap_retain_30pct,sufficiency_abs_gap_retain_30pct,retained_cells_30pct,deletion_auc_0_70_mean_logit_drop,variant,case_id,scale_stability_mean_spearman,scale_stability_w1_w3,scale_stability_w1_w5,scale_stability_w3_w5,spearman_w1_w3,spearman_w1_w5,spearman_w3_w5,mean_scale_spearman
+150,150,6.574392318725586e-05,0,0.3528418093919754,15,0.656341228634119,30,0.9214366972446442,45,0.856571227312088,75,0.8089453428983688,105,-0.13637715578079224,0.13637715578079224,15,-0.24227207899093628,0.24227207899093628,30,-0.301626980304718,0.301626980304718,45,0.7019226790538856,E0_EarlyConcat,01,0.920808332222173,0.9198862171652075,0.8752655673585493,0.9672732121427621,,,,
+150,150,9.638071060180664e-05,0,0.4211968034505844,15,0.8451054096221924,30,1.1640161871910095,45,1.085330307483673,75,0.9431635737419128,105,0.022571682929992676,0.022571682929992676,15,-0.014074176549911499,0.014074176549911499,30,-0.08179467916488647,0.08179467916488647,45,0.8751712675605501,E2_MoFE_Shapley,01,0.9189451383024432,0.9393999733321481,0.8687763900617806,0.9486590515134006,,,,
+150,150,9.638071060180664e-05,0,0.37026094645261765,15,0.7585245072841644,30,1.093913972377777,45,1.0734611749649048,75,0.8995215892791748,105,0.07217270135879517,0.07217270135879517,15,-0.0010961592197418213,0.0010961592197418213,30,-0.11648410558700562,0.11648410558700562,45,0.8308783631239619,E1_MoFE_Router,01,,,,,0.9393999733321481,0.8687763900617806,0.9486590515134006,0.9189451383024432
+150,150,6.29425048828125e-05,0,0.4557170271873474,15,0.6498788148164749,30,0.49386394023895264,45,0.43360528349876404,75,0.4492800533771515,105,-1.186801016330719,1.186801016330719,15,-0.8607568144798279,0.8607568144798279,30,-0.5515806078910828,0.5515806078910828,45,0.45184483485562466,E0_EarlyConcat,02,0.948875950042224,0.9600160007111427,0.9147090981821414,0.9719027512333882,,,,
+150,150,-8.589029312133789e-05,0,0.3460000604391098,15,0.7261224985122681,30,1.0725876092910767,45,0.9762407541275024,75,0.9002569913864136,105,0.347156822681427,0.347156822681427,15,0.32757896184921265,0.32757896184921265,30,0.2406596839427948,0.2406596839427948,45,0.7905285039118358,E2_MoFE_Shapley,02,0.9334251892676712,0.9514431752522334,0.8916822969909775,0.9571500955598028,,,,
+150,150,-8.589029312133789e-05,0,0.21258580684661865,15,0.5560037791728973,30,0.9516188502311707,45,0.8760976195335388,75,0.6876242756843567,105,0.14818128943443298,0.14818128943443298,15,0.21824857592582703,0.21824857592582703,30,0.19699817895889282,0.19699817895889282,45,0.6622563472815922,E1_MoFE_Router,02,,,,,0.9514431752522334,0.8916822969909775,0.9571500955598028,0.9334251892676712
+150,150,-4.982948303222656e-05,0,0.6246231161057949,15,1.1499897241592407,30,1.3267889618873596,45,1.2701305150985718,75,1.0392764806747437,105,-1.1954491138458252,1.1954491138458252,15,-0.7020494937896729,0.7020494937896729,30,-0.36489951610565186,0.36489951610565186,45,1.0491869827466354,E0_EarlyConcat,03,0.8497711009378195,0.8787857238099471,0.7552673452153429,0.9152602337881685,,,,
+150,150,1.5616416931152344e-05,0,0.7734843492507935,15,1.4450520873069763,30,1.477453887462616,45,1.3624622225761414,75,1.1028702780604362,105,-0.6596002578735352,0.6596002578735352,15,-0.3951551914215088,0.3951551914215088,30,-0.031283020973205566,0.031283020973205566,45,1.1803599713104111,E2_MoFE_Shapley,03,0.8143407855163934,0.8011111604960222,0.7062411662740566,0.9356700297791014,,,,
+150,150,1.5616416931152344e-05,0,0.32338035106658936,15,0.651199460029602,30,1.2304641157388687,45,1.2102992832660675,75,0.8741537779569626,105,0.10267245769500732,0.10267245769500732,15,0.22296690940856934,0.22296690940856934,30,0.11956650018692017,0.11956650018692017,45,0.8735765910574367,E1_MoFE_Router,03,,,,,0.8011111604960222,0.7062411662740566,0.9356700297791014,0.8143407855163934
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+150,150,-4.172325134277344e-05,0,0.7235184907913208,15,1.502412885427475,30,2.162756562232971,45,2.126149833202362,75,2.0004488229751587,105,-0.3664212226867676,0.3664212226867676,15,-0.22129333019256592,0.22129333019256592,30,-0.2742382287979126,0.2742382287979126,45,1.6746848353317807,E2_MoFE_Shapley,04,0.8118304517237803,0.8265487354993555,0.6978496822080982,0.9110929374638872,,,,
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+150,150,-3.0517578125e-05,0,-0.11791390180587769,15,-0.15233498811721802,30,0.0718986988067627,45,0.25638169050216675,75,0.235147625207901,105,0.4098636507987976,0.4098636507987976,15,0.23634150624275208,0.23634150624275208,30,0.05602717399597168,0.05602717399597168,45,0.08364212938717433,E0_EarlyConcat,19,0.8801404506866972,0.9072812124983334,0.7907640339570647,0.9423761056046936,,,,
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+150,150,0.00011563301086425781,0,0.5845876932144165,15,1.1854255199432373,30,1.852372944355011,45,2.0745863169431686,75,1.9082763697952032,105,-0.01696324348449707,0.01696324348449707,15,-0.09125590324401855,0.09125590324401855,30,-0.13144385814666748,0.13144385814666748,45,1.5151542071253061,E0_EarlyConcat,20,0.9261501992681157,0.9245086448286592,0.8884999333303703,0.9654420196453176,,,,
+150,150,0.00015747547149658203,0,0.6270503997802734,15,1.228735089302063,30,1.7906582932919264,45,1.8324708342552185,75,1.7891404461115599,105,0.16212236881256104,0.16212236881256104,15,0.018865227699279785,0.018865227699279785,30,-0.07442164421081543,0.07442164421081543,45,1.4279905401968531,E2_MoFE_Shapley,20,0.9423808465561433,0.9367225210009335,0.9124832214765102,0.9779367971909864,,,,
+150,150,0.00015747547149658203,0,0.5185912847518921,15,1.0959563255310059,30,1.6798647418618202,45,1.8519408702850342,75,1.7791931387037039,105,0.5129009485244751,0.5129009485244751,15,0.22020959854125977,0.22020959854125977,30,0.06388592720031738,0.06388592720031738,45,1.373928334297878,E1_MoFE_Router,20,,,,,0.9367225210009335,0.9124832214765102,0.9779367971909864,0.9423808465561433
diff --git a/final/output/q3/first_round/q3_method_comparison.csv b/final/output/q3/first_round/q3_method_comparison.csv
new file mode 100644
index 0000000..5bb23e6
--- /dev/null
+++ b/final/output/q3/first_round/q3_method_comparison.csv
@@ -0,0 +1,4 @@
+variant,backbone,explanation_method,validation_accuracy,validation_macro_f1,validation_mae,validation_rmse,validation_pearson,attachment4_cases,comprehensiveness_delete_10pct_mean,comprehensiveness_delete_30pct_mean,sufficiency_abs_gap_retain_10pct_mean,sufficiency_abs_gap_retain_30pct_mean,deletion_auc_0_70_mean_logit_drop,scale_stability_mean_spearman,router_shapley_mean_spearman,router_shapley_top1_agreement_rate
+E0_EarlyConcat,EarlyConcat + BiGRU,exact Shapley + multiscale occlusion,0.6236263736263736,0.5779072819396367,0.6614117622375488,0.8730906021125315,0.6092112225440192,20,0.4599139286205173,1.1217660129070282,0.5246159449219704,0.25422920733690263,0.9156549779565207,0.9047463374768046,,
+E1_MoFE_Router,MoFE-7 + MLP Router,"router weights, tested by counterfactual deletion",0.614010989010989,0.5661110562085216,0.6426214575767517,0.8499163895044349,0.6013617546095743,20,0.21003895588219165,0.8701988908462226,0.49544571414589883,0.24998579360544682,0.7925371142836022,,0.725,0.85
+E2_MoFE_Shapley,MoFE-7 + MLP Router,exact Shapley + multiscale occlusion,0.614010989010989,0.5661110562085216,0.6426214575767517,0.8499163895044349,0.6013617546095743,20,0.3515274651348591,0.9817972692660988,0.41155125787481667,0.19497831985354425,0.8371464796829968,0.8822506774459532,0.725,0.85
diff --git a/final/output/q3/first_round/run_manifest.json b/final/output/q3/first_round/run_manifest.json
new file mode 100644
index 0000000..fb39f46
--- /dev/null
+++ b/final/output/q3/first_round/run_manifest.json
@@ -0,0 +1,154 @@
+{
+  "experiment": "Q3 first-round hierarchical counterfactual evidence attribution",
+  "created_at_unix": 1790348609.3921933,
+  "elapsed_seconds": 25.209044933319092,
+  "seed": 20260924,
+  "device": "cuda",
+  "attachment4": {
+    "version_dir": "/home/gloamxun/modeling_zhaocui/E题数据/附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本",
+    "video_dir": "/home/gloamxun/modeling_zhaocui/E题数据/附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos"
+  },
+  "attachment4_version": "unaligned_50",
+  "attachment4_cases": 20,
+  "coordinate_mode": "relative normalized progress",
+  "physical_time_alignment": false,
+  "time_mapping_limit": "estimated clip seconds equal normalized progress times video duration; original unaligned rows have no physical timestamps",
+  "models": {
+    "E0_EarlyConcat": {
+      "checkpoint": "/home/gloamxun/modeling_zhaocui/final/experiments/q2/unaligned_deep_two_b128/models/B0_early_concat/seed_20260924/model_best.pt",
+      "sha256": "19b3d21690447a0b38e26dc840f15e213a1d07a37b1ff1fe480e64976da4186b"
+    },
+    "E1_E2_MoFE": {
+      "checkpoint": "/home/gloamxun/modeling_zhaocui/final/experiments/q2/unaligned_deep_two_b128/models/B5_mofe_mlp/seed_20260924/model_best.pt",
+      "sha256": "552b60e34102b346faa157012a32342e3806194f567570d260a0f99e0c98c3e8"
+    }
+  },
+  "scaler": {
+    "path": "/home/gloamxun/modeling_zhaocui/final/experiments/q2/unaligned_deep_two_b128/unaligned_50_robust_stats.npz",
+    "sha256": "e97c0ce405c9624074a66c901e2cd364a347a3dd262fd813df2e08e7d49bb369",
+    "fit": "Q2 official training rows only"
+  },
+  "shapley": {
+    "coalitions": [
+      [],
+      [
+        0
+      ],
+      [
+        1
+      ],
+      [
+        2
+      ],
+      [
+        0,
+        1
+      ],
+      [
+        0,
+        2
+      ],
+      [
+        1,
+        2
+      ],
+      [
+        0,
+        1,
+        2
+      ]
+    ],
+    "class_value": "full-input predicted-class logit, fixed class across coalitions",
+    "regression_value": "predicted intensity",
+    "exact_enumeration": true,
+    "max_completeness_residual": 4.440892098500626e-16
+  },
+  "interaction": "pairwise Shapley interaction index; positive values indicate synergistic logit/intensity interaction under this convention",
+  "local_evidence": {
+    "method": "leave out a 1, 3, or 5-bin contiguous window from one modality",
+    "windows": [
+      1,
+      3,
+      5
+    ],
+    "scale_weights": [
+      0.3333333333333333,
+      0.3333333333333333,
+      0.3333333333333333
+    ]
+  },
+  "router_note": "MoFE router exposure is an internal routing summary, not prediction contribution; compare its rank with exact Shapley and deletion faithfulness.",
+  "faithfulness": {
+    "score": "fixed predicted-class logit",
+    "comprehensiveness": "full score minus score after deleting top-ranked cells",
+    "sufficiency": "full score minus score with only top-ranked cells retained",
+    "deletion_auc_fraction_range": [
+      0.0,
+      0.7
+    ],
+    "mask_training_rates": [
+      0.0,
+      0.1,
+      0.3,
+      0.5,
+      0.7
+    ],
+    "limit": "isolated sparse masks may still differ from the contiguous masks used in training; 90% deletion/10% retention is not claimed as in-distribution"
+  },
+  "validation_metrics": {
+    "early_concat": {
+      "n": 728,
+      "accuracy": 0.6236263736263736,
+      "macro_f1": 0.5779072819396367,
+      "mae": 0.6614117622375488,
+      "rmse": 0.8730906021125315,
+      "pearson": 0.6092112225440192
+    },
+    "mofe": {
+      "n": 728,
+      "accuracy": 0.614010989010989,
+      "macro_f1": 0.5661110562085216,
+      "mae": 0.6426214575767517,
+      "rmse": 0.8499163895044349,
+      "pearson": 0.6013617546095743
+    }
+  },
+  "validation_error_examples": 318,
+  "q2_validation_reference": {
+    "EarlyConcat": {
+      "accuracy": 0.6236263736263736,
+      "macro_f1": 0.5779072819396367,
+      "mae": 0.6614056825637817,
+      "rmse": 0.8730861646443621,
+      "pearson": 0.6092134453424162
+    },
+    "MoFE-7": {
+      "accuracy": 0.614010989010989,
+      "macro_f1": 0.5661110562085216,
+      "mae": 0.6426358222961426,
+      "rmse": 0.8499269790699532,
+      "pearson": 0.6013646920407029
+    }
+  },
+  "tokenizer_available": true,
+  "outputs": [
+    "attachment4_predictions.csv",
+    "attachment4_modal_shapley.csv",
+    "attachment4_pairwise_interactions.csv",
+    "attachment4_local_evidence.csv",
+    "attachment4_router_profiles.csv",
+    "attachment4_router_local_evidence.csv",
+    "attachment4_evidence_segments.csv",
+    "faithfulness_by_sample.csv",
+    "q3_method_comparison.csv",
+    "explanation_cards/",
+    "evidence_profiles/",
+    "evidence_frames/",
+    "typical_explanation_card.md",
+    "validation_metrics.json",
+    "validation_predictions.csv",
+    "validation_errors.csv",
+    "validation_error_attribution.csv"
+  ],
+  "typical_explanation_case": "04"
+}
diff --git a/final/output/q3/first_round/typical_explanation_card.md b/final/output/q3/first_round/typical_explanation_card.md
new file mode 100644
index 0000000..c10cb0f
--- /dev/null
+++ b/final/output/q3/first_round/typical_explanation_card.md
@@ -0,0 +1,58 @@
+# Q3 explanation card — E2_MoFE_Shapley — 04
+
+## Prediction
+
+- Polarity: **negative**
+- Intensity: -0.942
+- Predicted-class confidence: 0.784
+- Source clip: 附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/videos/04.mp4
+
+## Exact modality Shapley
+
+Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.
+
+| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |
+|---|---:|---:|---:|---:|
+| text | +1.6040 | 0.789 | -1.1000 | 0.688 |
+| audio | -0.3985 | 0.196 | +0.4059 | 0.254 |
+| vision | -0.0300 | 0.015 | +0.0925 | 0.058 |
+
+Shapley completeness residuals: class -2.22e-16, intensity 0.00e+00.
+## Pairwise Shapley interaction
+
+| Pair | Class logit | Intensity |
+|---|---:|---:|
+| Text + Audio | +0.1932 | -0.1284 |
+| Text + Vision | +0.2459 | -0.1719 |
+| Audio + Vision | +0.2132 | -0.2422 |
+
+## Local evidence segments
+
+Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.
+
+- **text bins 43–45 (relative progress 0.860–0.920), estimated clip interval 6.01–6.43s** — supports_predicted_class; evidence: absolutely not
+- **text bins 6–7 (relative progress 0.120–0.160), estimated clip interval 0.84–1.12s** — supports_predicted_class; evidence: blow it
+- **audio bins 39–42 (relative progress 0.780–0.860), estimated clip interval 5.46–6.01s** — opposes_predicted_class; evidence: unaligned audio feature rows 106–117; review the linked source clip at the estimated relative span
+- **audio bins 12–12 (relative progress 0.240–0.260), estimated clip interval 1.68–1.82s** — supports_predicted_class; evidence: unaligned audio feature rows 32–35; review the linked source clip at the estimated relative span
+- **vision bins 39–41 (relative progress 0.780–0.840), estimated clip interval 5.46–5.87s** — supports_predicted_class; evidence: unaligned vision feature rows 80–86; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_04_vision_1.jpg)
+- **vision bins 31–32 (relative progress 0.620–0.660), estimated clip interval 4.34–4.62s** — supports_predicted_class; evidence: unaligned vision feature rows 63–67; candidate frame time is estimated from relative progress
+  - Candidate frame: ![estimated visual evidence](../../evidence_frames/E2_MoFE_Shapley_04_vision_2.jpg)
+
+![Local evidence profile](../../evidence_profiles/E2_MoFE_Shapley/04.png)
+
+## Faithfulness checks
+
+- Comprehensiveness after deleting the top 10% / 30% cells: +0.7235 / +2.1628 predicted-class logit.
+- Sufficiency gap when retaining the top 10% / 30%: -0.3664 / -0.2742. Smaller absolute gaps are better.
+- Mean deletion logit drop over 0–70% deletion: +1.6747.
+
+## Provenance limit
+
+Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.
+
+Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.
+
+## Transcript
+
+If I blow it at the team exercise, should I kiss my chances of cheering "GO BLUE" goodbye?] Absolutely not.
diff --git a/final/output/q3/first_round/validation_error_attribution.csv b/final/output/q3/first_round/validation_error_attribution.csv
new file mode 100644
index 0000000..6cf95f2
--- /dev/null
+++ b/final/output/q3/first_round/validation_error_attribution.csv
@@ -0,0 +1,101 @@
+model,sample_id,true_class_name,predicted_class_name,classification_correct,true_intensity,predicted_intensity,absolute_intensity_error,classification_margin_target,classification_margin_phi_sum_residual,regression_shapley_sum_residual,class_margin_phi_text,class_margin_share_text,regression_phi_text,regression_share_text,class_margin_phi_audio,class_margin_share_audio,regression_phi_audio,regression_share_audio,class_margin_phi_vision,class_margin_share_vision,regression_phi_vision,regression_share_vision,class_margin_dominant_modality
+early_concat,f_ZJ7L14oYQ$_$21,positive,negative,False,2.0,-1.7844704389572144,3.784470558166504,predicted_logit_minus_true_logit,-8.881784197001252e-16,4.440892098500626e-16,3.5526891698439913,0.8597273268913176,-1.7990464816490808,0.8254845929782911,0.4941883732875189,0.11959032407158332,-0.24160055319468177,0.11085735491082428,0.08546658356984457,0.02068234903709927,-0.1387352297703425,0.06365805211088466,text
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diff --git a/final/output/q3/first_round/validation_errors.csv b/final/output/q3/first_round/validation_errors.csv
new file mode 100644
index 0000000..09e5eaa
--- /dev/null
+++ b/final/output/q3/first_round/validation_errors.csv
@@ -0,0 +1,637 @@
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diff --git a/final/output/q3/first_round/validation_metrics.json b/final/output/q3/first_round/validation_metrics.json
new file mode 100644
index 0000000..48c3b49
--- /dev/null
+++ b/final/output/q3/first_round/validation_metrics.json
@@ -0,0 +1,18 @@
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diff --git a/final/output/q3/first_round/validation_predictions.csv b/final/output/q3/first_round/validation_predictions.csv
new file mode 100644
index 0000000..865a8ab
--- /dev/null
+++ b/final/output/q3/first_round/validation_predictions.csv
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+mofe,zhE_68GMJIk$_$5,2,positive,0,negative,False,1.3333333730697632,-0.356037437915802,1.68937087059021,0.5037029385566711,0.2299516201019287,0.2663455307483673
+mofe,zjYEBwXGD8I$_$13,0,negative,2,positive,False,-0.3333333432674408,0.7367475032806396,1.0700808763504028,0.021251477301120758,0.16519659757614136,0.8135519027709961
+mofe,zjYEBwXGD8I$_$15,2,positive,2,positive,True,0.3333333432674408,0.2727140486240387,0.0606192946434021,0.13495011627674103,0.4161513149738312,0.4488985240459442
+mofe,zjYEBwXGD8I$_$0,1,neutral,2,positive,False,0.0,0.7837539911270142,0.7837539911270142,0.01564873568713665,0.16914698481559753,0.8152043223381042
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+mofe,zjYEBwXGD8I$_$4,1,neutral,2,positive,False,0.0,0.5510867834091187,0.5510867834091187,0.052256789058446884,0.26902294158935547,0.6787202954292297
+mofe,zjYEBwXGD8I$_$9,2,positive,2,positive,True,0.3333333432674408,0.9756951928138733,0.6423618793487549,0.0074030449613928795,0.09638741612434387,0.8962095379829407
+mofe,zk2jTlAtvSU$_$3,1,neutral,2,positive,False,0.0,0.3633723258972168,0.3633723258972168,0.05118435621261597,0.4443923234939575,0.5044233798980713
diff --git a/final/output/q3/mofe_router_heatmap_examples.pdf b/final/output/q3/mofe_router_heatmap_examples.pdf
new file mode 100644
index 0000000..2561049
Binary files /dev/null and b/final/output/q3/mofe_router_heatmap_examples.pdf differ
diff --git a/final/output/q3/mofe_router_heatmap_examples.png b/final/output/q3/mofe_router_heatmap_examples.png
new file mode 100644
index 0000000..4f79f5d
Binary files /dev/null and b/final/output/q3/mofe_router_heatmap_examples.png differ
diff --git a/final/output/q3/mofe_router_heatmap_manifest.json b/final/output/q3/mofe_router_heatmap_manifest.json
new file mode 100644
index 0000000..b67be0c
--- /dev/null
+++ b/final/output/q3/mofe_router_heatmap_manifest.json
@@ -0,0 +1,144 @@
+{
+  "source": "Attachment 4 unaligned_50; Q2 MoFE seed_20260924 checkpoint and train-only robust scaler",
+  "device": "cuda",
+  "router_weight_definition": "alpha[t,e], the MoFE softmax weight assigned to each of the seven experts at a relative-progress bin; weights normalize over eligible experts for each bin",
+  "router_heatmap_color_scale_min": 0.0,
+  "router_heatmap_color_scale_max": 1.0,
+  "router_heatmap_color_norm": "PowerNorm gamma=0.5; colorbar ticks retain raw alpha values",
+  "conditions": [
+    {
+      "case_id": "02",
+      "condition": "完整输入",
+      "prediction": "neutral",
+      "intensity": 0.11008816212415695,
+      "confidence": 0.4620336592197418,
+      "visible_bin_counts": {
+        "text": {
+          "visible_bins": 50,
+          "total_bins": 50,
+          "missing_fraction": 0.0
+        },
+        "audio": {
+          "visible_bins": 50,
+          "total_bins": 50,
+          "missing_fraction": 0.0
+        },
+        "vision": {
+          "visible_bins": 50,
+          "total_bins": 50,
+          "missing_fraction": 0.0
+        }
+      },
+      "masked_ranges_0based_start_end_exclusive": {},
+      "router_text_share": 0.35845422744750977,
+      "router_audio_share": 0.28927531838417053,
+      "router_vision_share": 0.3522704541683197
+    },
+    {
+      "case_id": "02",
+      "condition": "残缺：Text / Audio 各遮蔽 30%",
+      "prediction": "positive",
+      "intensity": -0.13957589864730835,
+      "confidence": 0.3556613326072693,
+      "visible_bin_counts": {
+        "text": {
+          "visible_bins": 35,
+          "total_bins": 50,
+          "missing_fraction": 0.30000000000000004
+        },
+        "audio": {
+          "visible_bins": 35,
+          "total_bins": 50,
+          "missing_fraction": 0.30000000000000004
+        },
+        "vision": {
+          "visible_bins": 50,
+          "total_bins": 50,
+          "missing_fraction": 0.0
+        }
+      },
+      "masked_ranges_0based_start_end_exclusive": {
+        "text": [
+          5,
+          20
+        ],
+        "audio": [
+          18,
+          33
+        ]
+      },
+      "router_text_share": 0.2992672622203827,
+      "router_audio_share": 0.25927940011024475,
+      "router_vision_share": 0.44145339727401733
+    },
+    {
+      "case_id": "03",
+      "condition": "完整输入",
+      "prediction": "negative",
+      "intensity": -0.61025071144104,
+      "confidence": 0.6876164078712463,
+      "visible_bin_counts": {
+        "text": {
+          "visible_bins": 50,
+          "total_bins": 50,
+          "missing_fraction": 0.0
+        },
+        "audio": {
+          "visible_bins": 50,
+          "total_bins": 50,
+          "missing_fraction": 0.0
+        },
+        "vision": {
+          "visible_bins": 50,
+          "total_bins": 50,
+          "missing_fraction": 0.0
+        }
+      },
+      "masked_ranges_0based_start_end_exclusive": {},
+      "router_text_share": 0.35898107290267944,
+      "router_audio_share": 0.291629433631897,
+      "router_vision_share": 0.3493894934654236
+    },
+    {
+      "case_id": "03",
+      "condition": "残缺：Vision 遮蔽 30%",
+      "prediction": "negative",
+      "intensity": -0.6705005168914795,
+      "confidence": 0.7000513672828674,
+      "visible_bin_counts": {
+        "text": {
+          "visible_bins": 50,
+          "total_bins": 50,
+          "missing_fraction": 0.0
+        },
+        "audio": {
+          "visible_bins": 50,
+          "total_bins": 50,
+          "missing_fraction": 0.0
+        },
+        "vision": {
+          "visible_bins": 35,
+          "total_bins": 50,
+          "missing_fraction": 0.30000000000000004
+        }
+      },
+      "masked_ranges_0based_start_end_exclusive": {
+        "vision": [
+          18,
+          33
+        ]
+      },
+      "router_text_share": 0.39606449007987976,
+      "router_audio_share": 0.3422035872936249,
+      "router_vision_share": 0.26173192262649536
+    }
+  ],
+  "controlled_missing_note": "The partial examples are controlled masks, not native Attachment 4 missingness. The complete examples retain all 50 bins in all modalities.",
+  "relative_time_limit": "The unaligned rows have no physical timestamps; frames and waveform are shown against normalized progress only.",
+  "outputs": [
+    "mofe_router_heatmap_examples.png",
+    "mofe_router_heatmap_examples.pdf",
+    "mofe_router_heatmap_summary.csv",
+    "mofe_router_heatmap_scores.csv"
+  ]
+}
diff --git a/final/output/q3/mofe_router_heatmap_scores.csv b/final/output/q3/mofe_router_heatmap_scores.csv
new file mode 100644
index 0000000..b33380b
--- /dev/null
+++ b/final/output/q3/mofe_router_heatmap_scores.csv
@@ -0,0 +1,201 @@
+case_id,condition,bin_0based,relative_progress_start,relative_progress_end,text_visible,router_utility_text,audio_visible,router_utility_audio,vision_visible,router_utility_vision,alpha_T,alpha_A,alpha_V,alpha_TA,alpha_TV,alpha_AV,alpha_TAV
+02,完整输入,0,0.0,0.02,True,0.6319580078125,True,0.5047607421875,True,0.62286376953125,0.13824857771396637,0.1077457144856453,0.1406942456960678,0.1311006397008896,0.21620838344097137,0.11958900094032288,0.14641351997852325
+02,完整输入,1,0.02,0.04,True,0.632080078125,True,0.50360107421875,True,0.62359619140625,0.13754142820835114,0.10739270597696304,0.14163333177566528,0.1315096616744995,0.2172633409500122,0.11895525455474854,0.14570431411266327
+02,完整输入,2,0.04,0.06,True,0.632080078125,True,0.50408935546875,True,0.62322998046875,0.13787563145160675,0.10752776265144348,0.1411183476448059,0.13136322796344757,0.2169441431760788,0.1191713735461235,0.14599952101707458
+02,完整输入,3,0.06,0.08,True,0.632080078125,True,0.5057373046875,True,0.6214599609375,0.1405574530363083,0.10829660296440125,0.1389058530330658,0.12969636917114258,0.21466661989688873,0.12062648683786392,0.14725066721439362
+02,完整输入,4,0.08,0.1,True,0.631591796875,True,0.5103759765625,True,0.6195068359375,0.1425255537033081,0.10899865627288818,0.13722248375415802,0.12892866134643555,0.20987361669540405,0.12219297885894775,0.15025801956653595
+02,完整输入,5,0.1,0.12,True,0.631103515625,True,0.51190185546875,True,0.61761474609375,0.14584027230739594,0.10936537384986877,0.13636347651481628,0.12718942761421204,0.20591966807842255,0.12324963510036469,0.15207220613956451
+02,完整输入,6,0.12,0.14,True,0.631103515625,True,0.5107421875,True,0.6182861328125,0.14485599100589752,0.10925304144620895,0.13673707842826843,0.12752369046211243,0.20765814185142517,0.12291589379310608,0.15105615556240082
+02,完整输入,7,0.14,0.16,True,0.63134765625,True,0.51165771484375,True,0.6185302734375,0.14410871267318726,0.10917823016643524,0.1367313414812088,0.12815262377262115,0.20746994018554688,0.12282625585794449,0.15153296291828156
+02,完整输入,8,0.16,0.18,True,0.630859375,True,0.51202392578125,True,0.61859130859375,0.14444774389266968,0.10910981148481369,0.13708604872226715,0.12787015736103058,0.20650264620780945,0.12301260977983475,0.1519709974527359
+02,完整输入,9,0.18,0.2,True,0.6307373046875,True,0.5128173828125,True,0.61865234375,0.1441173553466797,0.10899436473846436,0.137245312333107,0.12818241119384766,0.20582741498947144,0.12303905189037323,0.15259410440921783
+02,完整输入,10,0.2,0.22,True,0.630615234375,True,0.51251220703125,True,0.618408203125,0.14486262202262878,0.10906780511140823,0.13715003430843353,0.1276889145374298,0.20543649792671204,0.12319617718458176,0.15259791910648346
+02,完整输入,11,0.22,0.24,True,0.630859375,True,0.51220703125,True,0.61846923828125,0.1442815065383911,0.10909823328256607,0.13701613247394562,0.1280471384525299,0.20643708109855652,0.12299551069736481,0.15212441980838776
+02,完整输入,12,0.24,0.26,True,0.6302490234375,True,0.51226806640625,True,0.6187744140625,0.14459040760993958,0.10895345360040665,0.13758045434951782,0.12767718732357025,0.20558342337608337,0.12316985428333282,0.1524452418088913
+02,完整输入,13,0.26,0.28,True,0.6304931640625,True,0.51153564453125,True,0.619140625,0.14399415254592896,0.1089443489909172,0.13764454424381256,0.1279175728559494,0.20684313774108887,0.12295260280370712,0.15170367062091827
+02,完整输入,14,0.28,0.3,True,0.630859375,True,0.51123046875,True,0.6192626953125,0.14336250722408295,0.10899937897920609,0.13743141293525696,0.12836429476737976,0.2079816460609436,0.12268012762069702,0.15118065476417542
+02,完整输入,15,0.3,0.32,True,0.630126953125,True,0.51129150390625,True,0.61883544921875,0.14501559734344482,0.10898473113775253,0.13774007558822632,0.127184197306633,0.2060118466615677,0.12321076542139053,0.1518527865409851
+02,完整输入,16,0.32,0.34,True,0.630126953125,True,0.5111083984375,True,0.618896484375,0.1448943316936493,0.10898372530937195,0.13772156834602356,0.1272452175617218,0.2062530219554901,0.12317296862602234,0.15172916650772095
+02,完整输入,17,0.34,0.36,True,0.6292724609375,True,0.51043701171875,True,0.61859130859375,0.14657467603683472,0.10891222208738327,0.13828811049461365,0.12592437863349915,0.20465415716171265,0.12361273169517517,0.152033731341362
+02,完整输入,18,0.36,0.38,True,0.629638671875,True,0.51092529296875,True,0.61767578125,0.14720597863197327,0.10916490107774734,0.1374620795249939,0.1258842647075653,0.20434774458408356,0.12366694957017899,0.15226803719997406
+02,完整输入,19,0.38,0.4,True,0.6309814453125,True,0.51287841796875,True,0.61822509765625,0.14460179209709167,0.10911916196346283,0.13688039779663086,0.12799254059791565,0.20565727353096008,0.12310409545898438,0.15264473855495453
+02,完整输入,20,0.4,0.42,True,0.6309814453125,True,0.512939453125,True,0.6182861328125,0.14478403329849243,0.10914097726345062,0.13684284687042236,0.12789061665534973,0.20550976693630219,0.1231478899717331,0.15268383920192719
+02,完整输入,21,0.42,0.44,True,0.630859375,True,0.51123046875,True,0.61956787109375,0.1429957151412964,0.10893267393112183,0.1376168131828308,0.12854565680027008,0.20820224285125732,0.12260375171899796,0.15110312402248383
+02,完整输入,22,0.44,0.46,True,0.6307373046875,True,0.50848388671875,True,0.61993408203125,0.14306776225566864,0.10887863487005234,0.13800585269927979,0.128099724650383,0.21034599840641022,0.12224995344877243,0.1493520587682724
+02,完整输入,23,0.46,0.48,True,0.6312255859375,True,0.50738525390625,True,0.62115478515625,0.1410960555076599,0.10853445529937744,0.1388721615076065,0.12925633788108826,0.212646946310997,0.12137763202190399,0.1482163965702057
+02,完整输入,24,0.48,0.5,True,0.6314697265625,True,0.50750732421875,True,0.62152099609375,0.13988478481769562,0.10838928818702698,0.1391391158103943,0.13015612959861755,0.21342939138412476,0.12091981619596481,0.14808154106140137
+02,完整输入,25,0.5,0.52,True,0.63134765625,True,0.509033203125,True,0.6207275390625,0.1409253478050232,0.1086704283952713,0.13831110298633575,0.12965112924575806,0.2116662561893463,0.12158455699682236,0.14919117093086243
+02,完整输入,26,0.52,0.54,True,0.6312255859375,True,0.510498046875,True,0.619873046875,0.1420627236366272,0.10886818915605545,0.1377440243959427,0.12909331917762756,0.20974907279014587,0.12217868864536285,0.15030400454998016
+02,完整输入,27,0.54,0.56,True,0.6307373046875,True,0.5107421875,True,0.61944580078125,0.14323318004608154,0.10895171016454697,0.1376262903213501,0.12832020223140717,0.20841915905475616,0.12261227518320084,0.15083719789981842
+02,完整输入,28,0.56,0.58,True,0.631103515625,True,0.51031494140625,True,0.62030029296875,0.14148657023906708,0.1087767481803894,0.1380048245191574,0.12941978871822357,0.21008828282356262,0.12203644961118698,0.15018729865550995
+02,完整输入,29,0.58,0.6,True,0.631103515625,True,0.50909423828125,True,0.6207275390625,0.14141160249710083,0.1086876168847084,0.1384652554988861,0.12921741604804993,0.2111273854970932,0.12181338667869568,0.14927728474140167
+02,完整输入,30,0.6,0.62,True,0.6309814453125,True,0.509521484375,True,0.620361328125,0.14160579442977905,0.10874458402395248,0.13821759819984436,0.1291896551847458,0.21063072979450226,0.12194932997226715,0.14966236054897308
+02,完整输入,31,0.62,0.64,True,0.6309814453125,True,0.51104736328125,True,0.61968994140625,0.14277535676956177,0.10891017317771912,0.13768096268177032,0.1286735236644745,0.20845741033554077,0.12252280861139297,0.15097978711128235
+02,完整输入,32,0.64,0.66,True,0.630859375,True,0.51202392578125,True,0.619140625,0.1435166448354721,0.10895711183547974,0.13747921586036682,0.1283642202615738,0.20699991285800934,0.12283279746770859,0.15185017883777618
+02,完整输入,33,0.66,0.68,True,0.630859375,True,0.51116943359375,True,0.6195068359375,0.14297901093959808,0.1089320257306099,0.13755561411380768,0.12857437133789062,0.20830821990966797,0.12258613109588623,0.15106463432312012
+02,完整输入,34,0.68,0.7,True,0.630615234375,True,0.50897216796875,True,0.61968994140625,0.14353834092617035,0.10891080647706985,0.13799455761909485,0.1277986317873001,0.20941489934921265,0.12251701205968857,0.14982569217681885
+02,完整输入,35,0.7,0.72,True,0.630859375,True,0.50750732421875,True,0.62060546875,0.1422322541475296,0.10868891328573227,0.1385970264673233,0.12846188247203827,0.21161876618862152,0.12184540927410126,0.1485556811094284
+02,完整输入,36,0.72,0.74,True,0.6309814453125,True,0.50616455078125,True,0.62054443359375,0.14281854033470154,0.10866573452949524,0.13857056200504303,0.12795831263065338,0.21239188313484192,0.12175257503986359,0.14784245193004608
+02,完整输入,37,0.74,0.76,True,0.6307373046875,True,0.5067138671875,True,0.62054443359375,0.14303341507911682,0.10870461165904999,0.1386292427778244,0.12778240442276,0.21167373657226562,0.12198042124509811,0.14819619059562683
+02,完整输入,38,0.76,0.78,True,0.63134765625,True,0.509765625,True,0.62054443359375,0.1411045789718628,0.10871157795190811,0.13820871710777283,0.12958647310733795,0.21091927587985992,0.12179391086101532,0.14967547357082367
+02,完整输入,39,0.78,0.8,True,0.631103515625,True,0.51068115234375,True,0.61981201171875,0.1421748399734497,0.1088731586933136,0.1376926600933075,0.12905244529247284,0.20935790240764618,0.12228105217218399,0.1505679339170456
+02,完整输入,40,0.8,0.82,True,0.631103515625,True,0.51141357421875,True,0.6199951171875,0.14190147817134857,0.10882814228534698,0.13770295679569244,0.12931637465953827,0.20900024473667145,0.12229710817337036,0.15095371007919312
+02,完整输入,41,0.82,0.84,True,0.630859375,True,0.50994873046875,True,0.61993408203125,0.14277026057243347,0.1088717132806778,0.13795536756515503,0.12845785915851593,0.20931608974933624,0.12239445000886917,0.15023434162139893
+02,完整输入,42,0.84,0.86,True,0.6304931640625,True,0.51226806640625,True,0.61907958984375,0.14380896091461182,0.1089077964425087,0.13761883974075317,0.12817028164863586,0.20627978444099426,0.1229766309261322,0.15223775804042816
+02,完整输入,43,0.86,0.88,True,0.6309814453125,True,0.5091552734375,True,0.62030029296875,0.14235471189022064,0.10880143195390701,0.13815177977085114,0.128616601228714,0.21039585769176483,0.12212612479925156,0.14955347776412964
+02,完整输入,44,0.88,0.9,True,0.631591796875,True,0.50494384765625,True,0.62225341796875,0.13971108198165894,0.1080414354801178,0.14008551836013794,0.12999771535396576,0.21521760523319244,0.12029639631509781,0.1466502696275711
+02,完整输入,45,0.9,0.92,True,0.6317138671875,True,0.50439453125,True,0.62322998046875,0.13774453103542328,0.10761162638664246,0.14119364321231842,0.13140349090099335,0.21654988825321198,0.11933229118585587,0.14616453647613525
+02,完整输入,46,0.92,0.94,True,0.6317138671875,True,0.5054931640625,True,0.6224365234375,0.13926321268081665,0.10803018510341644,0.1401296854019165,0.13036462664604187,0.21518607437610626,0.12021256238222122,0.14681363105773926
+02,完整输入,47,0.94,0.96,True,0.6317138671875,True,0.50543212890625,True,0.62225341796875,0.13929125666618347,0.10803921520709991,0.14004677534103394,0.13037371635437012,0.2151947319507599,0.12020796537399292,0.14684633910655975
+02,完整输入,48,0.96,0.98,True,0.6318359375,True,0.5037841796875,True,0.6231689453125,0.13858410716056824,0.10763711482286453,0.14113180339336395,0.13071177899837494,0.2166178971529007,0.11948885768651962,0.1458284854888916
+02,完整输入,49,0.98,1.0,True,0.6317138671875,True,0.5009765625,True,0.62445068359375,0.13708427548408508,0.10684632509946823,0.14325876533985138,0.131616473197937,0.2186528742313385,0.11812204122543335,0.14441920816898346
+02,残缺：Text / Audio 各遮蔽 30%,0,0.0,0.02,True,0.6319580078125,True,0.5047607421875,True,0.62286376953125,0.13824857771396637,0.1077457144856453,0.1406942456960678,0.1311006397008896,0.21620838344097137,0.11958900094032288,0.14641351997852325
+02,残缺：Text / Audio 各遮蔽 30%,1,0.02,0.04,True,0.632080078125,True,0.50360107421875,True,0.62359619140625,0.13754142820835114,0.10739270597696304,0.14163333177566528,0.1315096616744995,0.2172633409500122,0.11895525455474854,0.14570431411266327
+02,残缺：Text / Audio 各遮蔽 30%,2,0.04,0.06,True,0.632080078125,True,0.50408935546875,True,0.62322998046875,0.13787563145160675,0.10752776265144348,0.1411183476448059,0.13136322796344757,0.2169441431760788,0.1191713735461235,0.14599952101707458
+02,残缺：Text / Audio 各遮蔽 30%,3,0.06,0.08,True,0.63134765625,True,0.50726318359375,True,0.6162109375,0.1429402083158493,0.10958198457956314,0.13980136811733246,0.13109982013702393,0.2098950743675232,0.11916640400886536,0.14751513302326202
+02,残缺：Text / Audio 各遮蔽 30%,4,0.08,0.1,True,0.630615234375,True,0.51312255859375,True,0.61065673828125,0.14681580662727356,0.11122353374958038,0.13926729559898376,0.13129708170890808,0.2008444219827652,0.11898605525493622,0.1515657901763916
+02,残缺：Text / Audio 各遮蔽 30%,5,0.1,0.12,False,,True,0.66162109375,True,0.6640625,0.0,0.33605098724365234,0.3382605016231537,0.0,0.0,0.32568857073783875,0.0
+02,残缺：Text / Audio 各遮蔽 30%,6,0.12,0.14,False,,True,0.656982421875,True,0.661865234375,0.0,0.33822745084762573,0.3429895043373108,0.0,0.0,0.31878307461738586,0.0
+02,残缺：Text / Audio 各遮蔽 30%,7,0.14,0.16,False,,True,0.6533203125,True,0.65966796875,0.0,0.3403385281562805,0.34656932950019836,0.0,0.0,0.3130921423435211,0.0
+02,残缺：Text / Audio 各遮蔽 30%,8,0.16,0.18,False,,True,0.653076171875,True,0.66015625,0.0,0.3399646282196045,0.3469041585922241,0.0,0.0,0.313131183385849,0.0
+02,残缺：Text / Audio 各遮蔽 30%,9,0.18,0.2,False,,True,0.65283203125,True,0.660400390625,0.0,0.3395744860172272,0.34716805815696716,0.0,0.0,0.31325748562812805,0.0
+02,残缺：Text / Audio 各遮蔽 30%,10,0.2,0.22,False,,True,0.6533203125,True,0.66015625,0.0,0.3397434949874878,0.3467714786529541,0.0,0.0,0.3134850263595581,0.0
+02,残缺：Text / Audio 各遮蔽 30%,11,0.22,0.24,False,,True,0.653076171875,True,0.66015625,0.0,0.3399554491043091,0.34680986404418945,0.0,0.0,0.3132346570491791,0.0
+02,残缺：Text / Audio 各遮蔽 30%,12,0.24,0.26,False,,True,0.65234375,True,0.66064453125,0.0,0.3394160568714142,0.34754589200019836,0.0,0.0,0.31303802132606506,0.0
+02,残缺：Text / Audio 各遮蔽 30%,13,0.26,0.28,False,,True,0.652099609375,True,0.660400390625,0.0,0.33955228328704834,0.3479651212692261,0.0,0.0,0.31248265504837036,0.0
+02,残缺：Text / Audio 各遮蔽 30%,14,0.28,0.3,False,,True,0.652099609375,True,0.66015625,0.0,0.33985453844070435,0.34791868925094604,0.0,0.0,0.3122267723083496,0.0
+02,残缺：Text / Audio 各遮蔽 30%,15,0.3,0.32,False,,True,0.65234375,True,0.66064453125,0.0,0.33952897787094116,0.3477941155433655,0.0,0.0,0.31267693638801575,0.0
+02,残缺：Text / Audio 各遮蔽 30%,16,0.32,0.34,False,,True,0.649169921875,True,0.6640625,0.0,0.3358391523361206,0.3509140908718109,0.0,0.0,0.31324681639671326,0.0
+02,残缺：Text / Audio 各遮蔽 30%,17,0.34,0.36,False,,True,0.645263671875,True,0.6689453125,0.0,0.331234872341156,0.35486385226249695,0.0,0.0,0.3139013350009918,0.0
+02,残缺：Text / Audio 各遮蔽 30%,18,0.36,0.38,False,,False,,True,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0
+02,残缺：Text / Audio 各遮蔽 30%,19,0.38,0.4,False,,False,,True,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0
+02,残缺：Text / Audio 各遮蔽 30%,20,0.4,0.42,True,0.672119140625,False,,True,0.701171875,0.29867464303970337,0.0,0.327738493680954,0.0,0.37358683347702026,0.0,0.0
+02,残缺：Text / Audio 各遮蔽 30%,21,0.42,0.44,True,0.673583984375,False,,True,0.7060546875,0.29387110471725464,0.0,0.3264673054218292,0.0,0.3796616196632385,0.0,0.0
+02,残缺：Text / Audio 各遮蔽 30%,22,0.44,0.46,True,0.675537109375,False,,True,0.7080078125,0.2921953499317169,0.0,0.3246166408061981,0.0,0.38318800926208496,0.0,0.0
+02,残缺：Text / Audio 各遮蔽 30%,23,0.46,0.48,True,0.675537109375,False,,True,0.708984375,0.29092779755592346,0.0,0.3245522379875183,0.0,0.3845199942588806,0.0,0.0
+02,残缺：Text / Audio 各遮蔽 30%,24,0.48,0.5,True,0.67626953125,False,,True,0.7099609375,0.28997987508773804,0.0,0.32383421063423157,0.0,0.386185884475708,0.0,0.0
+02,残缺：Text / Audio 各遮蔽 30%,25,0.5,0.52,True,0.67626953125,False,,True,0.7099609375,0.29010483622550964,0.0,0.32373499870300293,0.0,0.3861602246761322,0.0,0.0
+02,残缺：Text / Audio 各遮蔽 30%,26,0.52,0.54,True,0.67626953125,False,,True,0.709716796875,0.29028502106666565,0.0,0.32374557852745056,0.0,0.3859693706035614,0.0,0.0
+02,残缺：Text / Audio 各遮蔽 30%,27,0.54,0.56,True,0.67578125,False,,True,0.708984375,0.29096418619155884,0.0,0.3241868317127228,0.0,0.3848489820957184,0.0,0.0
+02,残缺：Text / Audio 各遮蔽 30%,28,0.56,0.58,True,0.676025390625,False,,True,0.71044921875,0.2895053029060364,0.0,0.32400208711624146,0.0,0.38649263978004456,0.0,0.0
+02,残缺：Text / Audio 各遮蔽 30%,29,0.58,0.6,True,0.675537109375,False,,True,0.7099609375,0.29004019498825073,0.0,0.3245762884616852,0.0,0.3853835463523865,0.0,0.0
+02,残缺：Text / Audio 各遮蔽 30%,30,0.6,0.62,True,0.67578125,False,,True,0.7099609375,0.29001885652542114,0.0,0.32430848479270935,0.0,0.3856726288795471,0.0,0.0
+02,残缺：Text / Audio 各遮蔽 30%,31,0.62,0.64,True,0.6826171875,False,,True,0.7080078125,0.292213499546051,0.0,0.3175137937068939,0.0,0.39027273654937744,0.0,0.0
+02,残缺：Text / Audio 各遮蔽 30%,32,0.64,0.66,True,0.688720703125,False,,True,0.705810546875,0.2940535843372345,0.0,0.31114140152931213,0.0,0.394804984331131,0.0,0.0
+02,残缺：Text / Audio 各遮蔽 30%,33,0.66,0.68,True,0.6297607421875,True,0.51519775390625,True,0.62103271484375,0.14136101305484772,0.10633038729429245,0.14112752676010132,0.131265327334404,0.20231814682483673,0.12272364646196365,0.15487399697303772
+02,残缺：Text / Audio 各遮蔽 30%,34,0.68,0.7,True,0.6300048828125,True,0.51104736328125,True,0.62030029296875,0.14281204342842102,0.10769578814506531,0.13969583809375763,0.1291266232728958,0.2063746303319931,0.12262618541717529,0.15166889131069183
+02,残缺：Text / Audio 各遮蔽 30%,35,0.7,0.72,True,0.630859375,True,0.50750732421875,True,0.62060546875,0.1422322541475296,0.10868891328573227,0.1385970264673233,0.12846188247203827,0.21161876618862152,0.12184540927410126,0.1485556811094284
+02,残缺：Text / Audio 各遮蔽 30%,36,0.72,0.74,True,0.6309814453125,True,0.50616455078125,True,0.62054443359375,0.14281854033470154,0.10866573452949524,0.13857056200504303,0.12795831263065338,0.21239188313484192,0.12175257503986359,0.14784245193004608
+02,残缺：Text / Audio 各遮蔽 30%,37,0.74,0.76,True,0.6307373046875,True,0.5067138671875,True,0.62054443359375,0.14303341507911682,0.10870461165904999,0.1386292427778244,0.12778240442276,0.21167373657226562,0.12198042124509811,0.14819619059562683
+02,残缺：Text / Audio 各遮蔽 30%,38,0.76,0.78,True,0.63134765625,True,0.509765625,True,0.62054443359375,0.1411045789718628,0.10871157795190811,0.13820871710777283,0.12958647310733795,0.21091927587985992,0.12179391086101532,0.14967547357082367
+02,残缺：Text / Audio 各遮蔽 30%,39,0.78,0.8,True,0.631103515625,True,0.51068115234375,True,0.61981201171875,0.1421748399734497,0.1088731586933136,0.1376926600933075,0.12905244529247284,0.20935790240764618,0.12228105217218399,0.1505679339170456
+02,残缺：Text / Audio 各遮蔽 30%,40,0.8,0.82,True,0.631103515625,True,0.51141357421875,True,0.6199951171875,0.14190147817134857,0.10882814228534698,0.13770295679569244,0.12931637465953827,0.20900024473667145,0.12229710817337036,0.15095371007919312
+02,残缺：Text / Audio 各遮蔽 30%,41,0.82,0.84,True,0.630859375,True,0.50994873046875,True,0.61993408203125,0.14277026057243347,0.1088717132806778,0.13795536756515503,0.12845785915851593,0.20931608974933624,0.12239445000886917,0.15023434162139893
+02,残缺：Text / Audio 各遮蔽 30%,42,0.84,0.86,True,0.6304931640625,True,0.51226806640625,True,0.61907958984375,0.14380896091461182,0.1089077964425087,0.13761883974075317,0.12817028164863586,0.20627978444099426,0.1229766309261322,0.15223775804042816
+02,残缺：Text / Audio 各遮蔽 30%,43,0.86,0.88,True,0.6309814453125,True,0.5091552734375,True,0.62030029296875,0.14235471189022064,0.10880143195390701,0.13815177977085114,0.128616601228714,0.21039585769176483,0.12212612479925156,0.14955347776412964
+02,残缺：Text / Audio 各遮蔽 30%,44,0.88,0.9,True,0.631591796875,True,0.50494384765625,True,0.62225341796875,0.13971108198165894,0.1080414354801178,0.14008551836013794,0.12999771535396576,0.21521760523319244,0.12029639631509781,0.1466502696275711
+02,残缺：Text / Audio 各遮蔽 30%,45,0.9,0.92,True,0.6317138671875,True,0.50439453125,True,0.62322998046875,0.13774453103542328,0.10761162638664246,0.14119364321231842,0.13140349090099335,0.21654988825321198,0.11933229118585587,0.14616453647613525
+02,残缺：Text / Audio 各遮蔽 30%,46,0.92,0.94,True,0.6317138671875,True,0.5054931640625,True,0.6224365234375,0.13926321268081665,0.10803018510341644,0.1401296854019165,0.13036462664604187,0.21518607437610626,0.12021256238222122,0.14681363105773926
+02,残缺：Text / Audio 各遮蔽 30%,47,0.94,0.96,True,0.6317138671875,True,0.50543212890625,True,0.62225341796875,0.13929125666618347,0.10803921520709991,0.14004677534103394,0.13037371635437012,0.2151947319507599,0.12020796537399292,0.14684633910655975
+02,残缺：Text / Audio 各遮蔽 30%,48,0.96,0.98,True,0.6318359375,True,0.5037841796875,True,0.6231689453125,0.13858410716056824,0.10763711482286453,0.14113180339336395,0.13071177899837494,0.2166178971529007,0.11948885768651962,0.1458284854888916
+02,残缺：Text / Audio 各遮蔽 30%,49,0.98,1.0,True,0.6317138671875,True,0.5009765625,True,0.62445068359375,0.13708427548408508,0.10684632509946823,0.14325876533985138,0.131616473197937,0.2186528742313385,0.11812204122543335,0.14441920816898346
+03,完整输入,0,0.0,0.02,True,0.6343994140625,True,0.51568603515625,True,0.61236572265625,0.14943912625312805,0.11083990335464478,0.13120213150978088,0.12735557556152344,0.2036711573600769,0.12349830567836761,0.15399383008480072
+03,完整输入,1,0.02,0.04,True,0.6336669921875,True,0.51513671875,True,0.61456298828125,0.14711734652519226,0.11029059439897537,0.13275925815105438,0.1281014084815979,0.20499755442142487,0.12320444732904434,0.1535293459892273
+03,完整输入,2,0.04,0.06,True,0.6314697265625,True,0.513671875,True,0.61700439453125,0.1460743099451065,0.10944059491157532,0.13591773808002472,0.1275186538696289,0.20441851019859314,0.12335357069969177,0.15327665209770203
+03,完整输入,3,0.06,0.08,True,0.6302490234375,True,0.5125732421875,True,0.617919921875,0.14589200913906097,0.10911107063293457,0.13714638352394104,0.12706908583641052,0.204403355717659,0.12343187630176544,0.15294623374938965
+03,完整输入,4,0.08,0.1,True,0.6341552734375,True,0.51531982421875,True,0.61285400390625,0.14908373355865479,0.11072928458452225,0.1315513253211975,0.12736819684505463,0.20400561392307281,0.12346493452787399,0.15379692614078522
+03,完整输入,5,0.1,0.12,True,0.6328125,True,0.51318359375,True,0.61676025390625,0.14514566957950592,0.10970758646726608,0.13464859127998352,0.1283678561449051,0.20698919892311096,0.12288321554660797,0.15225791931152344
+03,完整输入,6,0.12,0.14,True,0.6326904296875,True,0.51397705078125,True,0.61663818359375,0.145215705037117,0.10972211509943008,0.1346437931060791,0.12845978140830994,0.20620162785053253,0.12297353148460388,0.15278343856334686
+03,完整输入,7,0.14,0.16,True,0.634033203125,True,0.5130615234375,True,0.6129150390625,0.15034271776676178,0.11068841069936752,0.131700336933136,0.12608571350574493,0.20500147342681885,0.12364428490400314,0.1525370180606842
+03,完整输入,8,0.16,0.18,True,0.6324462890625,True,0.5126953125,True,0.61468505859375,0.14885444939136505,0.11017264425754547,0.1337597519159317,0.1262989342212677,0.20477138459682465,0.12359549850225449,0.15254732966423035
+03,完整输入,9,0.18,0.2,True,0.6348876953125,True,0.51458740234375,True,0.61151123046875,0.15080727636814117,0.11106164753437042,0.13034790754318237,0.12656211853027344,0.2042543590068817,0.12362178415060043,0.15334494411945343
+03,完整输入,10,0.2,0.22,True,0.6331787109375,True,0.51416015625,True,0.61322021484375,0.14977805316448212,0.11053218692541122,0.13260947167873383,0.12645359337329865,0.2034331113100052,0.1236957237124443,0.1534978151321411
+03,完整输入,11,0.22,0.24,True,0.632568359375,True,0.51397705078125,True,0.614013671875,0.14948323369026184,0.11027759313583374,0.13356176018714905,0.12628433108329773,0.20311592519283295,0.12374959141016006,0.15352760255336761
+03,完整输入,12,0.24,0.26,True,0.6317138671875,True,0.51312255859375,True,0.61431884765625,0.14958864450454712,0.11013516783714294,0.13418112695217133,0.12590445578098297,0.2030627429485321,0.1238236129283905,0.15330426394939423
+03,完整输入,13,0.26,0.28,True,0.6324462890625,True,0.5140380859375,True,0.6136474609375,0.14977125823497772,0.11035294830799103,0.13337811827659607,0.12623970210552216,0.20279547572135925,0.12377781420946121,0.15368473529815674
+03,完整输入,14,0.28,0.3,True,0.6331787109375,True,0.5155029296875,True,0.61395263671875,0.1481342911720276,0.11030710488557816,0.13309159874916077,0.12754292786121368,0.20327015221118927,0.12347253412008286,0.15418139100074768
+03,完整输入,15,0.3,0.32,True,0.633544921875,True,0.5152587890625,True,0.61383056640625,0.1479862928390503,0.11042311787605286,0.13253764808177948,0.12771424651145935,0.20415325462818146,0.12336605042219162,0.15381941199302673
+03,完整输入,16,0.32,0.34,True,0.6341552734375,True,0.512451171875,True,0.61505126953125,0.14715878665447235,0.11017480492591858,0.13275028765201569,0.12753191590309143,0.20769640803337097,0.12296807020902634,0.15171967446804047
+03,完整输入,17,0.34,0.36,True,0.6329345703125,True,0.51123046875,True,0.61614990234375,0.14711716771125793,0.10990599542856216,0.13415756821632385,0.12692493200302124,0.20758868753910065,0.123100146651268,0.15120549499988556
+03,完整输入,18,0.36,0.38,True,0.6307373046875,True,0.51287841796875,True,0.6177978515625,0.145410418510437,0.10920459032058716,0.13670238852500916,0.12755773961544037,0.20497407019138336,0.12327411025762558,0.15287671983242035
+03,完整输入,19,0.38,0.4,True,0.6318359375,True,0.51373291015625,True,0.6168212890625,0.14568792283535004,0.10954993218183517,0.1353924721479416,0.12791956961154938,0.2052258551120758,0.12319467216730118,0.15302962064743042
+03,完整输入,20,0.4,0.42,True,0.633544921875,True,0.5147705078125,True,0.61383056640625,0.14808784425258636,0.11040165275335312,0.13259868323802948,0.12751656770706177,0.20451463758945465,0.12338186055421829,0.15349875390529633
+03,完整输入,21,0.42,0.44,True,0.63427734375,True,0.5146484375,True,0.61431884765625,0.1472557783126831,0.11041629314422607,0.1321430206298828,0.12808112800121307,0.20592962205410004,0.12310262769460678,0.1530715823173523
+03,完整输入,22,0.44,0.46,True,0.632080078125,True,0.5128173828125,True,0.6148681640625,0.14883357286453247,0.11008832603693008,0.13414248824119568,0.12623926997184753,0.20423761010169983,0.1236593946814537,0.15279936790466309
+03,完整输入,23,0.46,0.48,True,0.6336669921875,True,0.51531982421875,True,0.61187744140625,0.15048757195472717,0.1108522117137909,0.13162243366241455,0.12663954496383667,0.2024468630552292,0.12374012917280197,0.15421128273010254
+03,完整输入,24,0.48,0.5,True,0.6319580078125,True,0.51458740234375,True,0.61676025390625,0.14518320560455322,0.10950221121311188,0.1353132575750351,0.1284460723400116,0.20483259856700897,0.123128741979599,0.153593972325325
+03,完整输入,25,0.5,0.52,True,0.631103515625,True,0.51312255859375,True,0.6175537109375,0.1452038437128067,0.10930052399635315,0.13631218671798706,0.12785704433918,0.2052416354417801,0.12319325655698776,0.15289151668548584
+03,完整输入,26,0.52,0.54,True,0.6307373046875,True,0.51275634765625,True,0.616943359375,0.14694371819496155,0.1094164252281189,0.13633377850055695,0.12670071423053741,0.20403343439102173,0.12355533242225647,0.15301662683486938
+03,完整输入,27,0.54,0.56,True,0.6319580078125,True,0.51446533203125,True,0.6162109375,0.14627371728420258,0.10966487228870392,0.13504314422607422,0.12783397734165192,0.20421957969665527,0.1233212798833847,0.15364348888397217
+03,完整输入,28,0.56,0.58,True,0.6337890625,True,0.51434326171875,True,0.61572265625,0.145407035946846,0.11002085357904434,0.1332612782716751,0.1288314163684845,0.20690913498401642,0.12280520051717758,0.15276505053043365
+03,完整输入,29,0.58,0.6,True,0.634765625,True,0.5115966796875,True,0.6148681640625,0.14773227274417877,0.11021885275840759,0.13212214410305023,0.1272086501121521,0.20860517024993896,0.12290193885564804,0.1512109786272049
+03,完整输入,30,0.6,0.62,True,0.633056640625,True,0.5142822265625,True,0.6143798828125,0.14795565605163574,0.11025909334421158,0.13323287665843964,0.1273098737001419,0.20450367033481598,0.12342120707035065,0.1533176153898239
+03,完整输入,31,0.62,0.64,True,0.6337890625,True,0.51580810546875,True,0.61468505859375,0.14657826721668243,0.11023308336734772,0.13287968933582306,0.12853336334228516,0.2047206312417984,0.12313341349363327,0.15392158925533295
+03,完整输入,32,0.64,0.66,True,0.6334228515625,True,0.515869140625,True,0.61407470703125,0.14766289293766022,0.11030013859272003,0.13296473026275635,0.12792037427425385,0.20351044833660126,0.12337015569210052,0.15427125990390778
+03,完整输入,33,0.66,0.68,True,0.6351318359375,True,0.51605224609375,True,0.61248779296875,0.14854693412780762,0.11087007075548172,0.13071361184120178,0.12805725634098053,0.20461027324199677,0.1232491284608841,0.15395277738571167
+03,完整输入,34,0.68,0.7,True,0.6343994140625,True,0.51605224609375,True,0.612548828125,0.1488693207502365,0.1107717975974083,0.13136062026023865,0.12773041427135468,0.20364609360694885,0.12341569364070892,0.1542060524225235
+03,完整输入,35,0.7,0.72,True,0.632080078125,True,0.513671875,True,0.61572265625,0.14708305895328522,0.10985144227743149,0.13462938368320465,0.12729400396347046,0.20461514592170715,0.12340471148490906,0.15312229096889496
+03,完整输入,36,0.72,0.74,True,0.6304931640625,True,0.51177978515625,True,0.61724853515625,0.14727883040905,0.10936513543128967,0.136676624417305,0.12622007727622986,0.2042945921421051,0.12361618876457214,0.15254855155944824
+03,完整输入,37,0.74,0.76,True,0.6307373046875,True,0.51190185546875,True,0.61883544921875,0.14423491060733795,0.1090194582939148,0.13736487925052643,0.12789250910282135,0.20655962824821472,0.12300721555948257,0.15192142128944397
+03,完整输入,38,0.76,0.78,True,0.631591796875,True,0.512939453125,True,0.6185302734375,0.1433277577161789,0.10915801674127579,0.13650347292423248,0.12895505130290985,0.20712512731552124,0.12270890921354294,0.1522216945886612
+03,完整输入,39,0.78,0.8,True,0.630615234375,True,0.513671875,True,0.6185302734375,0.14441217482089996,0.10889311879873276,0.137347012758255,0.12812435626983643,0.2046414464712143,0.12318921834230423,0.15339265763759613
+03,完整输入,40,0.8,0.82,True,0.63037109375,True,0.51251220703125,True,0.61767578125,0.14595356583595276,0.10918184369802475,0.13692598044872284,0.12710608541965485,0.20452655851840973,0.12341424077749252,0.15289172530174255
+03,完整输入,41,0.82,0.84,True,0.6312255859375,True,0.5135498046875,True,0.61810302734375,0.14469826221466064,0.1091734915971756,0.13653895258903503,0.12815380096435547,0.20527337491512299,0.12313053756952286,0.153031587600708
+03,完整输入,42,0.84,0.86,True,0.631103515625,True,0.513671875,True,0.61767578125,0.1453820765018463,0.10924401134252548,0.13642200827598572,0.12780025601387024,0.20457375049591064,0.12327020615339279,0.1533077508211136
+03,完整输入,43,0.86,0.88,True,0.6309814453125,True,0.51348876953125,True,0.61700439453125,0.14643022418022156,0.10937659442424774,0.13621284067630768,0.1272110790014267,0.2038862705230713,0.12346360832452774,0.15341942012310028
+03,完整输入,44,0.88,0.9,True,0.6309814453125,True,0.512939453125,True,0.61737060546875,0.146013543009758,0.10932491719722748,0.13640987873077393,0.12728294730186462,0.20465224981307983,0.12337246537208557,0.15294399857521057
+03,完整输入,45,0.9,0.92,True,0.6297607421875,True,0.5126953125,True,0.6182861328125,0.14602608978748322,0.10890321433544159,0.13781677186489105,0.12683768570423126,0.2034870684146881,0.12353978306055069,0.1533893197774887
+03,完整输入,46,0.92,0.94,True,0.6326904296875,True,0.513916015625,True,0.6131591796875,0.15015240013599396,0.11047862470149994,0.1329924315214157,0.1261155605316162,0.20283810794353485,0.12380850315093994,0.15361444652080536
+03,完整输入,47,0.94,0.96,True,0.6334228515625,True,0.5140380859375,True,0.61572265625,0.14574378728866577,0.10998307168483734,0.13359294831752777,0.12846636772155762,0.20659692585468292,0.12292414158582687,0.1526927649974823
+03,完整输入,48,0.96,0.98,True,0.63037109375,True,0.51263427734375,True,0.6190185546875,0.14386796951293945,0.10884086787700653,0.137720987200737,0.1281718611717224,0.20570282638072968,0.12305891513824463,0.1526365727186203
+03,完整输入,49,0.98,1.0,True,0.6309814453125,True,0.51312255859375,True,0.61724853515625,0.14601677656173706,0.10935701429843903,0.13628770411014557,0.1273491382598877,0.20461584627628326,0.12335619330406189,0.1530173271894455
+03,残缺：Vision 遮蔽 30%,0,0.0,0.02,True,0.6343994140625,True,0.51568603515625,True,0.61236572265625,0.14943912625312805,0.11083990335464478,0.13120213150978088,0.12735557556152344,0.2036711573600769,0.12349830567836761,0.15399383008480072
+03,残缺：Vision 遮蔽 30%,1,0.02,0.04,True,0.6336669921875,True,0.51513671875,True,0.61456298828125,0.14711734652519226,0.11029059439897537,0.13275925815105438,0.1281014084815979,0.20499755442142487,0.12320444732904434,0.1535293459892273
+03,残缺：Vision 遮蔽 30%,2,0.04,0.06,True,0.6314697265625,True,0.513671875,True,0.61700439453125,0.1460743099451065,0.10944059491157532,0.13591773808002472,0.1275186538696289,0.20441851019859314,0.12335357069969177,0.15327665209770203
+03,残缺：Vision 遮蔽 30%,3,0.06,0.08,True,0.6302490234375,True,0.5125732421875,True,0.617919921875,0.14589200913906097,0.10911107063293457,0.13714638352394104,0.12706908583641052,0.204403355717659,0.12343187630176544,0.15294623374938965
+03,残缺：Vision 遮蔽 30%,4,0.08,0.1,True,0.6341552734375,True,0.51531982421875,True,0.61285400390625,0.14908373355865479,0.11072928458452225,0.1315513253211975,0.12736819684505463,0.20400561392307281,0.12346493452787399,0.15379692614078522
+03,残缺：Vision 遮蔽 30%,5,0.1,0.12,True,0.6328125,True,0.51318359375,True,0.61676025390625,0.14514566957950592,0.10970758646726608,0.13464859127998352,0.1283678561449051,0.20698919892311096,0.12288321554660797,0.15225791931152344
+03,残缺：Vision 遮蔽 30%,6,0.12,0.14,True,0.6326904296875,True,0.51397705078125,True,0.61663818359375,0.145215705037117,0.10972211509943008,0.1346437931060791,0.12845978140830994,0.20620162785053253,0.12297353148460388,0.15278343856334686
+03,残缺：Vision 遮蔽 30%,7,0.14,0.16,True,0.634033203125,True,0.5130615234375,True,0.6129150390625,0.15034271776676178,0.11068841069936752,0.131700336933136,0.12608571350574493,0.20500147342681885,0.12364428490400314,0.1525370180606842
+03,残缺：Vision 遮蔽 30%,8,0.16,0.18,True,0.6324462890625,True,0.5126953125,True,0.61468505859375,0.14885444939136505,0.11017264425754547,0.1337597519159317,0.1262989342212677,0.20477138459682465,0.12359549850225449,0.15254732966423035
+03,残缺：Vision 遮蔽 30%,9,0.18,0.2,True,0.6348876953125,True,0.51458740234375,True,0.61151123046875,0.15080727636814117,0.11106164753437042,0.13034790754318237,0.12656211853027344,0.2042543590068817,0.12362178415060043,0.15334494411945343
+03,残缺：Vision 遮蔽 30%,10,0.2,0.22,True,0.6331787109375,True,0.51416015625,True,0.61322021484375,0.14977805316448212,0.11053218692541122,0.13260947167873383,0.12645359337329865,0.2034331113100052,0.1236957237124443,0.1534978151321411
+03,残缺：Vision 遮蔽 30%,11,0.22,0.24,True,0.632568359375,True,0.51397705078125,True,0.614013671875,0.14948323369026184,0.11027759313583374,0.13356176018714905,0.12628433108329773,0.20311592519283295,0.12374959141016006,0.15352760255336761
+03,残缺：Vision 遮蔽 30%,12,0.24,0.26,True,0.6317138671875,True,0.51312255859375,True,0.61431884765625,0.14958864450454712,0.11013516783714294,0.13418112695217133,0.12590445578098297,0.2030627429485321,0.1238236129283905,0.15330426394939423
+03,残缺：Vision 遮蔽 30%,13,0.26,0.28,True,0.6324462890625,True,0.5140380859375,True,0.6136474609375,0.14977125823497772,0.11035294830799103,0.13337811827659607,0.12623970210552216,0.20279547572135925,0.12377781420946121,0.15368473529815674
+03,残缺：Vision 遮蔽 30%,14,0.28,0.3,True,0.6331787109375,True,0.5155029296875,True,0.61395263671875,0.1481342911720276,0.11030710488557816,0.13309159874916077,0.12754292786121368,0.20327015221118927,0.12347253412008286,0.15418139100074768
+03,残缺：Vision 遮蔽 30%,15,0.3,0.32,True,0.633544921875,True,0.5152587890625,True,0.61383056640625,0.1479862928390503,0.11042311787605286,0.13253764808177948,0.12771424651145935,0.20415325462818146,0.12336605042219162,0.15381941199302673
+03,残缺：Vision 遮蔽 30%,16,0.32,0.34,True,0.631103515625,True,0.517333984375,True,0.61419677734375,0.14446622133255005,0.11185542494058609,0.13279321789741516,0.12945210933685303,0.205330953001976,0.12407900393009186,0.152023047208786
+03,残缺：Vision 遮蔽 30%,17,0.34,0.36,True,0.62744140625,True,0.52117919921875,True,0.614013671875,0.1418534219264984,0.11322852224111557,0.13407501578330994,0.1308392584323883,0.20285898447036743,0.12528441846370697,0.15186044573783875
+03,残缺：Vision 遮蔽 30%,18,0.36,0.38,True,0.703125,True,0.6669921875,False,,0.3330003619194031,0.29676738381385803,0.0,0.37023231387138367,0.0,0.0,0.0
+03,残缺：Vision 遮蔽 30%,19,0.38,0.4,True,0.700439453125,True,0.674072265625,False,,0.32591870427131653,0.2995849847793579,0.0,0.37449631094932556,0.0,0.0,0.0
+03,残缺：Vision 遮蔽 30%,20,0.4,0.42,True,0.69775390625,True,0.67919921875,False,,0.32082247734069824,0.3022778332233429,0.0,0.37689968943595886,0.0,0.0,0.0
+03,残缺：Vision 遮蔽 30%,21,0.42,0.44,True,0.697509765625,True,0.681396484375,False,,0.3186020851135254,0.3024008572101593,0.0,0.3789970576763153,0.0,0.0,0.0
+03,残缺：Vision 遮蔽 30%,22,0.44,0.46,True,0.697265625,True,0.67626953125,False,,0.3236031234264374,0.3025128245353699,0.0,0.37388408184051514,0.0,0.0,0.0
+03,残缺：Vision 遮蔽 30%,23,0.46,0.48,True,0.69775390625,True,0.67626953125,False,,0.3238431513309479,0.3021487891674042,0.0,0.37400802969932556,0.0,0.0,0.0
+03,残缺：Vision 遮蔽 30%,24,0.48,0.5,True,0.6982421875,True,0.6806640625,False,,0.31954535841941833,0.301895409822464,0.0,0.37855926156044006,0.0,0.0,0.0
+03,残缺：Vision 遮蔽 30%,25,0.5,0.52,True,0.69775390625,True,0.6796875,False,,0.3203459084033966,0.3021802008152008,0.0,0.37747395038604736,0.0,0.0,0.0
+03,残缺：Vision 遮蔽 30%,26,0.52,0.54,True,0.69775390625,True,0.6767578125,False,,0.32326436042785645,0.30227887630462646,0.0,0.3744567930698395,0.0,0.0,0.0
+03,残缺：Vision 遮蔽 30%,27,0.54,0.56,True,0.697998046875,True,0.678955078125,False,,0.320993036031723,0.3019968271255493,0.0,0.37701013684272766,0.0,0.0,0.0
+03,残缺：Vision 遮蔽 30%,28,0.56,0.58,True,0.697509765625,True,0.68310546875,False,,0.3167448043823242,0.30227720737457275,0.0,0.380977988243103,0.0,0.0,0.0
+03,残缺：Vision 遮蔽 30%,29,0.58,0.6,True,0.697265625,True,0.681640625,False,,0.318287193775177,0.3027157783508301,0.0,0.3789970874786377,0.0,0.0,0.0
+03,残缺：Vision 遮蔽 30%,30,0.6,0.62,True,0.69775390625,True,0.6787109375,False,,0.32131239771842957,0.3023238480091095,0.0,0.3763637840747833,0.0,0.0,0.0
+03,残缺：Vision 遮蔽 30%,31,0.62,0.64,True,0.70068359375,True,0.67529296875,False,,0.3246828019618988,0.2993685007095337,0.0,0.3759487271308899,0.0,0.0,0.0
+03,残缺：Vision 遮蔽 30%,32,0.64,0.66,True,0.70361328125,True,0.667236328125,False,,0.33275046944618225,0.29639148712158203,0.0,0.3708580732345581,0.0,0.0,0.0
+03,残缺：Vision 遮蔽 30%,33,0.66,0.68,True,0.6295166015625,True,0.52593994140625,True,0.61083984375,0.1431836038827896,0.11432524770498276,0.13078205287456512,0.13171276450157166,0.2001621425151825,0.12543806433677673,0.1543961465358734
+03,残缺：Vision 遮蔽 30%,34,0.68,0.7,True,0.631591796875,True,0.5208740234375,True,0.6116943359375,0.14620906114578247,0.11256798356771469,0.13132619857788086,0.129523366689682,0.2015291303396225,0.12453090399503708,0.1543133407831192
+03,残缺：Vision 遮蔽 30%,35,0.7,0.72,True,0.632080078125,True,0.513671875,True,0.61572265625,0.14708305895328522,0.10985144227743149,0.13462938368320465,0.12729400396347046,0.20461514592170715,0.12340471148490906,0.15312229096889496
+03,残缺：Vision 遮蔽 30%,36,0.72,0.74,True,0.6304931640625,True,0.51177978515625,True,0.61724853515625,0.14727883040905,0.10936513543128967,0.136676624417305,0.12622007727622986,0.2042945921421051,0.12361618876457214,0.15254855155944824
+03,残缺：Vision 遮蔽 30%,37,0.74,0.76,True,0.6307373046875,True,0.51190185546875,True,0.61883544921875,0.14423491060733795,0.1090194582939148,0.13736487925052643,0.12789250910282135,0.20655962824821472,0.12300721555948257,0.15192142128944397
+03,残缺：Vision 遮蔽 30%,38,0.76,0.78,True,0.631591796875,True,0.512939453125,True,0.6185302734375,0.1433277577161789,0.10915801674127579,0.13650347292423248,0.12895505130290985,0.20712512731552124,0.12270890921354294,0.1522216945886612
+03,残缺：Vision 遮蔽 30%,39,0.78,0.8,True,0.630615234375,True,0.513671875,True,0.6185302734375,0.14441217482089996,0.10889311879873276,0.137347012758255,0.12812435626983643,0.2046414464712143,0.12318921834230423,0.15339265763759613
+03,残缺：Vision 遮蔽 30%,40,0.8,0.82,True,0.63037109375,True,0.51251220703125,True,0.61767578125,0.14595356583595276,0.10918184369802475,0.13692598044872284,0.12710608541965485,0.20452655851840973,0.12341424077749252,0.15289172530174255
+03,残缺：Vision 遮蔽 30%,41,0.82,0.84,True,0.6312255859375,True,0.5135498046875,True,0.61810302734375,0.14469826221466064,0.1091734915971756,0.13653895258903503,0.12815380096435547,0.20527337491512299,0.12313053756952286,0.153031587600708
+03,残缺：Vision 遮蔽 30%,42,0.84,0.86,True,0.631103515625,True,0.513671875,True,0.61767578125,0.1453820765018463,0.10924401134252548,0.13642200827598572,0.12780025601387024,0.20457375049591064,0.12327020615339279,0.1533077508211136
+03,残缺：Vision 遮蔽 30%,43,0.86,0.88,True,0.6309814453125,True,0.51348876953125,True,0.61700439453125,0.14643022418022156,0.10937659442424774,0.13621284067630768,0.1272110790014267,0.2038862705230713,0.12346360832452774,0.15341942012310028
+03,残缺：Vision 遮蔽 30%,44,0.88,0.9,True,0.6309814453125,True,0.512939453125,True,0.61737060546875,0.146013543009758,0.10932491719722748,0.13640987873077393,0.12728294730186462,0.20465224981307983,0.12337246537208557,0.15294399857521057
+03,残缺：Vision 遮蔽 30%,45,0.9,0.92,True,0.6297607421875,True,0.5126953125,True,0.6182861328125,0.14602608978748322,0.10890321433544159,0.13781677186489105,0.12683768570423126,0.2034870684146881,0.12353978306055069,0.1533893197774887
+03,残缺：Vision 遮蔽 30%,46,0.92,0.94,True,0.6326904296875,True,0.513916015625,True,0.6131591796875,0.15015240013599396,0.11047862470149994,0.1329924315214157,0.1261155605316162,0.20283810794353485,0.12380850315093994,0.15361444652080536
+03,残缺：Vision 遮蔽 30%,47,0.94,0.96,True,0.6334228515625,True,0.5140380859375,True,0.61572265625,0.14574378728866577,0.10998307168483734,0.13359294831752777,0.12846636772155762,0.20659692585468292,0.12292414158582687,0.1526927649974823
+03,残缺：Vision 遮蔽 30%,48,0.96,0.98,True,0.63037109375,True,0.51263427734375,True,0.6190185546875,0.14386796951293945,0.10884086787700653,0.137720987200737,0.1281718611717224,0.20570282638072968,0.12305891513824463,0.1526365727186203
+03,残缺：Vision 遮蔽 30%,49,0.98,1.0,True,0.6309814453125,True,0.51312255859375,True,0.61724853515625,0.14601677656173706,0.10935701429843903,0.13628770411014557,0.1273491382598877,0.20461584627628326,0.12335619330406189,0.1530173271894455
diff --git a/final/output/q3/mofe_router_heatmap_summary.csv b/final/output/q3/mofe_router_heatmap_summary.csv
new file mode 100644
index 0000000..3ec36d5
--- /dev/null
+++ b/final/output/q3/mofe_router_heatmap_summary.csv
@@ -0,0 +1,5 @@
+case_id,condition,prediction,intensity,confidence,visible_bin_counts,masked_ranges_0based_start_end_exclusive,router_text_share,router_audio_share,router_vision_share
+02,完整输入,neutral,0.11008816212415695,0.4620336592197418,"{'text': {'visible_bins': 50, 'total_bins': 50, 'missing_fraction': 0.0}, 'audio': {'visible_bins': 50, 'total_bins': 50, 'missing_fraction': 0.0}, 'vision': {'visible_bins': 50, 'total_bins': 50, 'missing_fraction': 0.0}}",{},0.35845422744750977,0.28927531838417053,0.3522704541683197
+02,残缺：Text / Audio 各遮蔽 30%,positive,-0.13957589864730835,0.3556613326072693,"{'text': {'visible_bins': 35, 'total_bins': 50, 'missing_fraction': 0.30000000000000004}, 'audio': {'visible_bins': 35, 'total_bins': 50, 'missing_fraction': 0.30000000000000004}, 'vision': {'visible_bins': 50, 'total_bins': 50, 'missing_fraction': 0.0}}","{'text': (5, 20), 'audio': (18, 33)}",0.2992672622203827,0.25927940011024475,0.44145339727401733
+03,完整输入,negative,-0.61025071144104,0.6876164078712463,"{'text': {'visible_bins': 50, 'total_bins': 50, 'missing_fraction': 0.0}, 'audio': {'visible_bins': 50, 'total_bins': 50, 'missing_fraction': 0.0}, 'vision': {'visible_bins': 50, 'total_bins': 50, 'missing_fraction': 0.0}}",{},0.35898107290267944,0.291629433631897,0.3493894934654236
+03,残缺：Vision 遮蔽 30%,negative,-0.6705005168914795,0.7000513672828674,"{'text': {'visible_bins': 50, 'total_bins': 50, 'missing_fraction': 0.0}, 'audio': {'visible_bins': 50, 'total_bins': 50, 'missing_fraction': 0.0}, 'vision': {'visible_bins': 35, 'total_bins': 50, 'missing_fraction': 0.30000000000000004}}","{'vision': (18, 33)}",0.39606449007987976,0.3422035872936249,0.26173192262649536
diff --git a/final/q3/README.md b/final/q3/README.md
new file mode 100644
index 0000000..ae18619
--- /dev/null
+++ b/final/q3/README.md
@@ -0,0 +1,67 @@
+# Q3：分层反事实证据归因
+
+## 第一轮比较
+
+第一轮直接复用 Q2 官方 unaligned_50 实验中的 EarlyConcat + BiGRU、MoFE-7 + MLP Router 权重和 train-only robust scaler。这样 E0/E1/E2 使用固定预测器，主要比较解释方式。
+
+| 方案 | 预测器 | 解释 |
+|---|---|---|
+| E0 | EarlyConcat + BiGRU | 三模态精确 Shapley、配对交互、多尺度局部遮蔽 |
+| E1 | MoFE-7 + MLP Router | Router 模态/位置权重；用反事实删除检验其是否 faithful |
+| E2 | 与 E1 相同的 MoFE 检查点 | 三模态精确 Shapley、配对交互、多尺度局部遮蔽 |
+
+E1 与 E2 的预测逐样本相同。E1 的路由权重只描述融合机制，只有通过删除检验后才能说明它在这些样本上是否与预测行为一致。
+
+## 运行
+
+从项目根目录执行，附件目录由 FINAL_DATA_DIR 指定。它应包含 附件2-数据集特征文件/unaligned_50.pkl 和 附件4-可解释专项视频样本与特征文件/。
+
+~~~bash
+export FINAL_DATA_DIR="/path/to/task-data"
+python -m final.q3.run_experiments \
+  --output-dir final/output/q3/first_round \
+  --device auto
+~~~
+
+检查点与 scaler 默认读取 final/experiments/q2/unaligned_deep_two_b128/。完整运行会计算官方验证集预测及误差归因。--skip-validation 跳过这一步；--no-frames 跳过候选帧抽取。每轮实验使用新的空输出目录；若上次运行中断且留下部分文件，可对该目录增加 --resume 重新生成结果。
+
+## 方法定义
+
+令三个模态为 (M={T,A,V})。对每个附件 4 样本完整计算 8 个 coalition。分类价值函数使用完整输入预测类别的 logit，并在所有 coalition 上固定该类别；回归价值函数使用模型情感强度输出。绝对 Shapley 贡献除以三模态绝对贡献之和，得到模态作用比例；带符号值保留支持或反对预测的方向。配对交互采用标准 Shapley interaction index 系数。
+
+局部证据对每个可见模态位置计算窗口宽度 (win{1,3,5}) 的遮蔽前后差值，三个尺度等权平均。每个模态选取局部贡献绝对值最高的 10% 位置，合并相邻位置，并输出代表证据段。
+
+Faithfulness 使用固定预测类别 logit。Comprehensiveness 比较完整输入与删去高排名证据后的分数；sufficiency 比较完整输入与只保留高排名证据后的分数；deletion AUC 汇总删除 0% 到 70% 的分数下降。另记录宽度 1/3/5 局部图之间的 Spearman 相关作为尺度稳定性诊断。MoFE Router 与精确 Shapley 按样本比较 Spearman 排序相关和主导模态一致率。
+
+## 输出
+
+- attachment4_predictions.csv：E0、E1、E2 对 20 个样本的完整预测与类别概率。
+- attachment4_modal_shapley.csv：分类与强度的有符号贡献、绝对比例和完备性残差。
+- attachment4_pairwise_interactions.csv：Text–Audio、Text–Vision、Audio–Vision 交互。
+- attachment4_local_evidence.csv：E0/E2 的 1/3/5-bin 遮蔽差值和来源行。
+- attachment4_router_profiles.csv、attachment4_router_local_evidence.csv：MoFE 的专家权重和逐位置 Router utility。
+- attachment4_evidence_segments.csv：稀疏代表证据段、来源行、转写文本和候选视频时间。
+- faithfulness_by_sample.csv、q3_method_comparison.csv：逐样本与方案汇总的删除/保留检查。
+- validation_predictions.csv、validation_errors.csv、validation_error_attribution.csv：官方验证集指标、误差样本和分类 margin 归因。
+- explanation_cards/、typical_explanation_card.md、evidence_profiles/：逐样本解释卡、代表卡和 3×50 热图。
+- evidence_frames/：从原始视频抽取的候选帧。
+- run_manifest.json：检查点、scaler、输入模式、公式、运行边界与产物清单。
+
+## Router 热力图样例
+
+在完成 Q3 第一轮后，还可以绘制附件 4 样本 02、03 的输入遮蔽与 Router 权重对照图。每个样本分别展示原始完整输入和受控残缺输入：样本 02 遮蔽 30% 文本与音频，样本 03 遮蔽 30% 视觉。蓝色斜线只标输入中被遮蔽的位置；紧接着一行把七个 expert（T、A、V、TA、TV、AV、TAV）横向排列，色块/数字显示样本平均 Router 权重，下方窄条显示 50 个 bin 上的 α[t,e]。
+
+~~~bash
+export FINAL_DATA_DIR="/path/to/task-data"
+python -m final.q3.plot_router_heatmaps --output-dir final/output/q3
+~~~
+
+这两个残缺样例是人为遮蔽的对照，不是附件 4 的原生缺失数据。文本 token 按序列顺序显示，蓝色斜线标出对应遮蔽词段；音频波形和视频帧按相对进程显示蓝色遮蔽区。每个 bin 的七个 expert 权重在可用专家集合内归一化，未满足模态条件的 expert 权重为 0；色条使用原始 0–1 数值并通过平方根归一化提高低权重的可读性。样例顶部另列 T/A/V 的总体 Router exposure share。视频帧和音轨只按归一化进程投影，不能当作精确词/帧同步。Router 权重反映融合路由，不是预测贡献或情绪因果解释。
+
+图表与原始数据输出为 `mofe_router_heatmap_examples.png`、`mofe_router_heatmap_examples.pdf`、`mofe_router_heatmap_scores.csv`（逐 bin 七个 α 分数及模态可见掩码）、`mofe_router_heatmap_summary.csv` 和 `mofe_router_heatmap_manifest.json`。
+
+## 回溯边界
+
+附件 4 的文本、音频和视觉序列为未对齐特征，没有逐词、逐音频帧或逐视频帧的真实时间戳。输出会从 adapter 的稀疏投影权重记录来源特征行和归一化进程。视频候选秒数由归一化进程乘视频时长估算，只供人工回看；它不是真实物理时间对齐。文本 token 需要本地缓存 google-bert/bert-base-uncased tokenizer；缓存不存在时，解释卡仍保留完整转写和来源行。
+
+这些解释衡量的是当前预测器对输入遮蔽的响应，不是现实情绪成因。虽然模型在 Q2 训练时见过模态遮蔽，局部孤立遮蔽和只保留 10% 的输入仍可能偏离训练分布；相关数值按诊断结果报告，不称为解释准确率或因果效应。
diff --git a/final/q3/plot_router_heatmaps.py b/final/q3/plot_router_heatmaps.py
new file mode 100644
index 0000000..c8dcd18
--- /dev/null
+++ b/final/q3/plot_router_heatmaps.py
@@ -0,0 +1,463 @@
+"""Render input mask overlays followed by per-time weights for the seven MoFE experts.
+
+Expert weights are internal routing signals, not causal or counterfactual
+modality contribution scores.
+"""
+from __future__ import annotations
+
+import argparse
+import csv
+import json
+import subprocess
+from pathlib import Path
+from typing import Any
+
+import matplotlib
+
+matplotlib.use("Agg")
+matplotlib.rcParams["font.family"] = "sans-serif"
+matplotlib.rcParams["font.sans-serif"] = ["FandolHei", "DejaVu Sans"]
+matplotlib.rcParams["axes.unicode_minus"] = False
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+from matplotlib.patches import Rectangle
+from PIL import Image
+from scipy import sparse
+
+from . import run_experiments as q3
+
+
+MODALITY_INDEX = {name: i for i, name in enumerate(q3.MODALITIES)}
+CLASS_NAMES_ZH = {"negative": "负向", "neutral": "中性", "positive": "正向"}
+MASK_COLOR = "#21a6df"
+FRAME_PROGRESS = np.linspace(0.06, 0.94, 9)
+SAMPLE_CONDITIONS = (
+    ("02", "完整输入", {}),
+    ("02", "残缺：Text / Audio 各遮蔽 30%", {"text": (5, 20), "audio": (18, 33)}),
+    ("03", "完整输入", {}),
+    ("03", "残缺：Vision 遮蔽 30%", {"vision": (18, 33)}),
+)
+
+
+def _get_tokenizer() -> Any:
+    if q3.AutoTokenizer is None:
+        return None
+    try:
+        return q3.AutoTokenizer.from_pretrained("google-bert/bert-base-uncased", local_files_only=True)
+    except (OSError, ValueError):
+        return None
+
+
+def _load_audio(video_path: Path) -> tuple[np.ndarray, int] | None:
+    command = [
+        "ffmpeg", "-hide_banner", "-loglevel", "error", "-i", str(video_path),
+        "-map", "0:a:0", "-ac", "1", "-ar", "8000", "-f", "f32le", "pipe:1",
+    ]
+    try:
+        proc = subprocess.run(command, check=True, capture_output=True, timeout=45)
+        wave = np.frombuffer(proc.stdout, dtype="<f4").copy()
+        if not len(wave):
+            return None
+        return wave, 8000
+    except (OSError, subprocess.SubprocessError):
+        return None
+
+
+def _load_frame(video_path: Path, duration: float | None, progress: float) -> np.ndarray | None:
+    second = float(progress) * float(duration or 0.0)
+    if duration:
+        second = min(max(0.0, second), max(0.0, duration - 0.04))
+    command = [
+        "ffmpeg", "-hide_banner", "-loglevel", "error", "-ss", f"{second:.4f}",
+        "-i", str(video_path), "-frames:v", "1", "-vf", "scale=360:-2",
+        "-f", "image2pipe", "-vcodec", "mjpeg", "pipe:1",
+    ]
+    try:
+        proc = subprocess.run(command, check=True, capture_output=True, timeout=30)
+        import io
+
+        return np.asarray(Image.open(io.BytesIO(proc.stdout)).convert("RGB"))
+    except (OSError, subprocess.SubprocessError, ValueError):
+        return None
+
+
+def _source_words(case: dict[str, Any], tokenizer: Any) -> list[tuple[str, list[int]]]:
+    token_ids = np.asarray(case["text_bert"][0], dtype=np.int64)
+    attention = np.asarray(case["text_bert"][1], dtype=bool)
+    token_strings = tokenizer.convert_ids_to_tokens(token_ids.tolist())
+    words: list[tuple[str, list[int]]] = []
+    punctuation = set(".,!?;:%)]}’'\"…")
+    for source_index, token in enumerate(token_strings):
+        if not attention[source_index] or token in tokenizer.all_special_tokens:
+            continue
+        token = str(token)
+        if token.startswith("##") and words:
+            text, indices = words[-1]
+            words[-1] = (text + token[2:], indices + [source_index])
+        elif token in punctuation and words:
+            text, indices = words[-1]
+            words[-1] = (text + token, indices + [source_index])
+        else:
+            words.append((token, [source_index]))
+    return words
+
+
+def _draw_text_mask(ax: plt.Axes, case: dict[str, Any], mask: np.ndarray, tokenizer: Any) -> None:
+    words = _source_words(case, tokenizer)
+    ax.set_xlim(0, 1)
+    ax.set_ylim(0, 1)
+    ax.axis("off")
+    if not words:
+        ax.text(0.02, 0.52, "无可显示的有效文本 token", fontsize=8, color="#555")
+        return
+    x, y = 0.015, 0.78
+    char_width = 0.0125
+    line_height = 0.29
+    source_to_target = sparse.csc_matrix(case["provenance"]["text"].source_weights)
+    for word, source_indices in words:
+        label = word.replace("Ġ", "").replace("▁", "")
+        width = max(0.035, char_width * (len(label) + 1) + 0.016)
+        if x + width > 0.99:
+            x = 0.015
+            y -= line_height
+        if y < 0.05:
+            break
+        target_bins: set[int] = set()
+        for source_index in source_indices:
+            target_bins.update(
+                int(slot) for slot in source_to_target.getcol(source_index).tocoo().row
+            )
+        hidden = (
+            any(not mask[slot, MODALITY_INDEX["text"]] for slot in target_bins)
+            if target_bins
+            else False
+        )
+        face = MASK_COLOR if hidden else "#f0f2f5"
+        edge = "#0879ad" if hidden else "#c5cbd1"
+        ax.add_patch(
+            Rectangle((x, y - 0.12), width, 0.21, transform=ax.transAxes,
+                      facecolor=face, edgecolor=edge, linewidth=0.45, clip_on=True,
+                      hatch="//" if hidden else None)
+        )
+        ax.text(x + 0.008, y - 0.015, label, transform=ax.transAxes,
+                ha="left", va="center", fontsize=6.8, color="#fff" if hidden else "#111", clip_on=True)
+        x += width + 0.007
+    ax.text(0.015, 0.02, "文本按 token 顺序显示；蓝色斜线区域表示被遮蔽词段",
+            transform=ax.transAxes, fontsize=6.2, color="#555", va="bottom")
+
+
+def _draw_wave_mask(ax: plt.Axes, wave: np.ndarray | None, mask: np.ndarray) -> None:
+    ax.set_xlim(0, 1)
+    ax.set_ylim(-1.08, 1.08)
+    if wave is None:
+        ax.text(0.5, 0.5, "视频中未读取到音轨", ha="center", va="center", transform=ax.transAxes, fontsize=8)
+        ax.set_xticks([])
+        ax.set_yticks([])
+        return
+    step = max(1, len(wave) // 14000)
+    values = wave[::step]
+    peak = float(np.percentile(np.abs(values), 99.5)) if len(values) else 0.0
+    if peak > 1e-8:
+        values = np.clip(values / peak, -1.0, 1.0)
+    x = np.linspace(0, 1, len(values), endpoint=False)
+    if len(values) >= 2:
+        ax.plot(x, values, color="#333a43", linewidth=0.45, rasterized=True, zorder=2)
+    for slot in np.flatnonzero(~mask[:, MODALITY_INDEX["audio"]]):
+        ax.axvspan(slot / 50, (slot + 1) / 50, facecolor=MASK_COLOR, alpha=0.20,
+                   zorder=1, hatch="///", edgecolor="#0879ad", linewidth=0.0)
+    ax.axhline(0, color="#777", linewidth=0.45, alpha=0.5)
+    ax.set_yticks([])
+    ax.set_xticks([0, .25, .5, .75, 1.0])
+    ax.set_xticklabels(["0", ".25", ".50", ".75", "1"], fontsize=7)
+    ax.grid(axis="x", color="#bbb", alpha=0.25, linewidth=0.4)
+
+
+def _draw_video_mask(
+    ax: plt.Axes,
+    frames: list[np.ndarray | None],
+    mask: np.ndarray,
+) -> None:
+    ax.set_xlim(0, 1)
+    ax.set_ylim(0, 1)
+    ax.set_yticks([])
+    ax.set_xticks([0, .25, .5, .75, 1.0])
+    ax.set_xticklabels(["0", ".25", ".50", ".75", "1"], fontsize=7)
+    ax.grid(axis="x", color="#bbb", alpha=0.25, linewidth=0.4)
+    n = len(FRAME_PROGRESS)
+    cell = 1.0 / n
+    for index, (progress, frame) in enumerate(zip(FRAME_PROGRESS, frames)):
+        left = index * cell + 0.012
+        width = cell - 0.024
+        slot = min(int(progress * 50), 49)
+        present = bool(mask[slot, MODALITY_INDEX["vision"]])
+        if frame is None:
+            ax.add_patch(Rectangle((left, .12), width, .76, transform=ax.transAxes,
+                                   facecolor="#d5d8dc", edgecolor="#888", linewidth=0.7))
+            ax.text(left + width / 2, .5, "无帧", transform=ax.transAxes, ha="center", va="center", fontsize=6)
+        else:
+            rgb = frame.astype(np.float32) / 255
+            if not present:
+                tint = np.asarray(matplotlib.colors.to_rgb(MASK_COLOR), dtype=np.float32)
+                rgb = np.clip(0.64 * rgb + 0.36 * tint, 0, 1)
+            ax.imshow(rgb, extent=(left, left + width, .12, .88), aspect="auto", origin="upper")
+            if not present:
+                ax.add_patch(Rectangle((left, .12), width, .76, transform=ax.transAxes,
+                                       facecolor=MASK_COLOR, alpha=0.16, edgecolor="none"))
+            ax.add_patch(Rectangle((left, .12), width, .76, transform=ax.transAxes,
+                                   fill=False, edgecolor="#6a7075" if present else "#0879ad",
+                                   linewidth=0.75 if present else 1.2,
+                                   hatch=None if present else "///"))
+        ax.text(left + width / 2, .04, f"{progress:.2f}", transform=ax.transAxes,
+                ha="center", va="bottom", fontsize=6, color="#555")
+
+
+def _controlled_mask(base_mask: np.ndarray, missing_ranges: dict[str, tuple[int, int]]) -> np.ndarray:
+    mask = base_mask.copy()
+    for modality, (start, end) in missing_ranges.items():
+        index = MODALITY_INDEX[modality]
+        mask[start:end, index] = False
+    return mask
+
+
+def _draw_router_weights(
+    ax: plt.Axes,
+    expert_weights: np.ndarray,
+    norm: Any,
+    cmap: Any,
+) -> None:
+    ax.set_xlim(0, len(q3.EXPERT_NAMES))
+    ax.set_ylim(0, 1)
+    ax.axis("off")
+    for expert_index, expert_name in enumerate(q3.EXPERT_NAMES):
+        left = expert_index + 0.06
+        width = 0.88
+        mean_weight = float(expert_weights[:, expert_index].mean())
+        face = cmap(norm(mean_weight))
+        luminance = 0.2126 * face[0] + 0.7152 * face[1] + 0.0722 * face[2]
+        foreground = "#111" if luminance > 0.58 else "#fff"
+        ax.add_patch(Rectangle((left, 0.58), width, 0.31, facecolor=face,
+                               edgecolor="#8a8f96", linewidth=0.55))
+        ax.text(left + width / 2, 0.79, expert_name, ha="center", va="center",
+                fontsize=8, color=foreground)
+        ax.text(left + width / 2, 0.65, f"{mean_weight:.3f}", ha="center", va="center",
+                fontsize=7, color=foreground)
+        for slot, value in enumerate(expert_weights[:, expert_index]):
+            cell_left = left + width * slot / expert_weights.shape[0]
+            ax.add_patch(Rectangle(
+                (cell_left, 0.22), width / expert_weights.shape[0], 0.18,
+                facecolor=cmap(norm(float(value))), edgecolor="white", linewidth=0.1,
+            ))
+
+
+def _mask_summary(mask: np.ndarray) -> dict[str, Any]:
+    return {
+        name: {
+            "visible_bins": int(mask[:, index].sum()),
+            "total_bins": int(mask.shape[0]),
+            "missing_fraction": float(1 - mask[:, index].mean()),
+        }
+        for index, name in enumerate(q3.MODALITIES)
+    }
+
+
+def create_plot(args: argparse.Namespace) -> None:
+    torch.set_num_threads(4)
+    device = torch.device("cuda" if args.device == "auto" and torch.cuda.is_available()
+                          else ("cpu" if args.device == "auto" else args.device))
+    if device.type == "cuda":
+        torch.set_float32_matmul_precision("high")
+    output_dir = args.output_dir.expanduser().resolve()
+    output_dir.mkdir(parents=True, exist_ok=True)
+    centers, scales = q3._load_scaler(args.scaler.expanduser().resolve())
+    cases, _ = q3._read_attachment4("unaligned_50")
+    by_id = {case["case_id"]: case for case in cases}
+    needed = sorted({case_id for case_id, _, _ in SAMPLE_CONDITIONS})
+    missing = [case_id for case_id in needed if case_id not in by_id]
+    if missing:
+        raise KeyError(f"sample IDs not found in Attachment 4: {missing}")
+    dims = tuple(int(x.shape[-1]) for x in by_id[needed[0]]["features"])
+    model = q3._build_model("mofe", dims, args.checkpoint.expanduser().resolve(), device)
+    router_cmap = plt.get_cmap("magma")
+    tokenizer = _get_tokenizer()
+    if tokenizer is None:
+        raise RuntimeError("local google-bert/bert-base-uncased tokenizer is required to label text tokens")
+
+    case_cache: dict[str, dict[str, Any]] = {}
+    audio_cache: dict[str, np.ndarray | None] = {}
+    frame_cache: dict[tuple[str, int], np.ndarray | None] = {}
+    columns: list[dict[str, Any]] = []
+    score_rows: list[dict[str, Any]] = []
+    summaries: list[dict[str, Any]] = []
+    for case_id, condition, missing_ranges in SAMPLE_CONDITIONS:
+        case = case_cache.setdefault(case_id, by_id[case_id])
+        base_mask = case["mask"]
+        mask = _controlled_mask(base_mask, missing_ranges)
+        features = q3._scale_features(case["features"], base_mask, centers, scales)
+        output = q3._model_output(
+            model,
+            tuple(torch.as_tensor(x[None], dtype=torch.float32, device=device) for x in features),
+            torch.as_tensor(mask[None], dtype=torch.bool, device=device),
+        )
+        logits = output["logits"][0].float().cpu().numpy()
+        expert_weights = output["alpha"][0].float().cpu().numpy()
+        pred = int(logits.argmax())
+        confidence = float(torch.softmax(output["logits"][0].float(), dim=-1)[pred].cpu().item())
+        profile_row, utility = q3._router_profile(model, features, mask, device)
+        prediction_name = q3.CLASS_NAMES[pred]
+        modalities_summary = _mask_summary(mask)
+        if case_id not in audio_cache:
+            loaded_audio = _load_audio(case["video_file"]) if case["video_file"] else None
+            audio_cache[case_id] = loaded_audio[0] if loaded_audio is not None else None
+        wave = audio_cache[case_id]
+        frames = []
+        for frame_index, progress in enumerate(FRAME_PROGRESS):
+            key = (case_id, frame_index)
+            if key not in frame_cache:
+                frame_cache[key] = _load_frame(case["video_file"], case["video_duration_sec"], float(progress)) \
+                    if case["video_file"] else None
+            frames.append(frame_cache[key])
+        columns.append({
+            "case": case,
+            "condition": condition,
+            "mask": mask,
+            "utility": utility,
+            "expert_weights": expert_weights,
+            "wave": wave,
+            "frames": frames,
+            "prediction": prediction_name,
+            "intensity": float(output["intensity"][0].float().cpu().item()),
+            "confidence": confidence,
+            "profile": profile_row,
+        })
+        for slot in range(mask.shape[0]):
+            score_row = {
+                "case_id": case_id,
+                "condition": condition,
+                "bin_0based": slot,
+                "relative_progress_start": slot / mask.shape[0],
+                "relative_progress_end": (slot + 1) / mask.shape[0],
+            }
+            for modality_index, modality in enumerate(q3.MODALITIES):
+                score_row[f"{modality}_visible"] = bool(mask[slot, modality_index])
+                score_row[f"router_utility_{modality}"] = (
+                    float(utility[modality_index, slot]) if mask[slot, modality_index] else ""
+                )
+            for expert_index, expert_name in enumerate(q3.EXPERT_NAMES):
+                score_row[f"alpha_{expert_name}"] = float(expert_weights[slot, expert_index])
+            score_rows.append(score_row)
+        summary_row = {
+            "case_id": case_id,
+            "condition": condition,
+            "prediction": prediction_name,
+            "intensity": float(output["intensity"][0].float().cpu().item()),
+            "confidence": confidence,
+            "visible_bin_counts": modalities_summary,
+            "masked_ranges_0based_start_end_exclusive": missing_ranges,
+            "router_text_share": profile_row["router_text_share"],
+            "router_audio_share": profile_row["router_audio_share"],
+            "router_vision_share": profile_row["router_vision_share"],
+        }
+        summaries.append(summary_row)
+
+    router_norm = matplotlib.colors.PowerNorm(gamma=0.5, vmin=0.0, vmax=1.0, clip=True)
+
+    fig = plt.figure(figsize=(19.5, 12.8), facecolor="white")
+    grid = fig.add_gridspec(
+        nrows=4, ncols=4, left=0.065, right=0.92, top=0.86, bottom=0.14,
+        height_ratios=(0.95, 1.1, 1.35, 1.25), hspace=0.34, wspace=0.12,
+    )
+    for column_index, item in enumerate(columns):
+        case_id = item["case"]["case_id"]
+        shares = [item["profile"][f"router_{name}_share"] for name in q3.MODALITIES]
+        modality_label = " / ".join(f"{name[0].upper()} {share:.0%}" for name, share in zip(q3.MODALITIES, shares))
+        title = f"样本 {case_id}｜{item['condition']}\n{CLASS_NAMES_ZH[item['prediction']]}，置信度 {item['confidence']:.2f}；Router 暴露 {modality_label}"
+        text_ax = fig.add_subplot(grid[0, column_index])
+        _draw_text_mask(text_ax, item["case"], item["mask"], tokenizer)
+        text_ax.set_title(title, loc="left", fontsize=9, pad=6, fontweight="normal")
+        wave_ax = fig.add_subplot(grid[1, column_index])
+        _draw_wave_mask(wave_ax, item["wave"], item["mask"])
+        if column_index == 0:
+            wave_ax.set_ylabel("Audio waveform\n归一化振幅", fontsize=7)
+        video_ax = fig.add_subplot(grid[2, column_index])
+        _draw_video_mask(video_ax, item["frames"], item["mask"])
+        if column_index == 0:
+            video_ax.set_ylabel("Vision frames", fontsize=7)
+        video_ax.set_xlabel("相对进程（非真实时间戳）", fontsize=7)
+        router_ax = fig.add_subplot(grid[3, column_index])
+        _draw_router_weights(router_ax, item["expert_weights"], router_norm, router_cmap)
+        for ax in (wave_ax, video_ax, router_ax):
+            ax.tick_params(axis="x", labelsize=7, pad=2)
+    fig.suptitle(
+        "先看输入遮蔽，再看 MoFE 七个 Router 专家的权重",
+        fontsize=16, fontweight="normal", y=0.96,
+    )
+    fig.text(
+        0.5, 0.91,
+        "蓝色斜线 = 输入被遮蔽；下方 T、A、V、TA、TV、AV、TAV 七张卡横排：大色块/数字是样本平均 α，窄条从左到右显示 50 个 bin 的 α[t,e]。",
+        ha="center", fontsize=9, color="#444",
+    )
+    fig.text(
+        0.5, 0.075,
+        "样本 02 残缺列遮蔽 Text bins 6–20 与 Audio bins 19–33；样本 03 残缺列遮蔽 Vision bins 19–33。每段均为 15/50 = 30%，完整列保留原始输入。",
+        ha="center", fontsize=8, color="#444",
+    )
+    fig.text(
+        0.5, 0.048,
+        "文本高亮按 token 顺序对应 bin；音频波形与视频帧按归一化进程展示。附件 4 无可信逐帧时间戳，Router 权重只描述专家路由。",
+        ha="center", fontsize=8, color="#666",
+    )
+    legend = Rectangle((0, 0), 1, 1, facecolor=MASK_COLOR, alpha=0.28,
+                       edgecolor="#0879ad", hatch="///", label="输入被遮蔽")
+    fig.legend(handles=[legend], loc="lower center", bbox_to_anchor=(0.5, 0.105),
+               frameon=False, ncol=1, fontsize=8)
+    scalar = matplotlib.cm.ScalarMappable(norm=router_norm, cmap=router_cmap)
+    cax = fig.add_axes([0.935, 0.24, 0.018, 0.42])
+    colorbar = fig.colorbar(scalar, cax=cax)
+    colorbar.set_label("Expert router weight α[t,e]", fontsize=8)
+    colorbar.set_ticks(np.linspace(0, 1, 5))
+    colorbar.ax.tick_params(labelsize=7)
+    png_path = output_dir / "mofe_router_heatmap_examples.png"
+    pdf_path = output_dir / "mofe_router_heatmap_examples.pdf"
+    fig.savefig(png_path, dpi=300, bbox_inches="tight")
+    fig.savefig(pdf_path, bbox_inches="tight")
+    plt.close(fig)
+
+    def write_csv(path: Path, rows: list[dict[str, Any]]) -> None:
+        with path.open("w", newline="", encoding="utf-8-sig") as stream:
+            writer = csv.DictWriter(stream, fieldnames=list(rows[0]))
+            writer.writeheader()
+            writer.writerows(rows)
+
+    write_csv(output_dir / "mofe_router_heatmap_summary.csv", summaries)
+    write_csv(output_dir / "mofe_router_heatmap_scores.csv", score_rows)
+    manifest = {
+        "source": "Attachment 4 unaligned_50; Q2 MoFE seed_20260924 checkpoint and train-only robust scaler",
+        "device": str(device),
+        "router_weight_definition": "alpha[t,e], the MoFE softmax weight assigned to each of the seven experts at a relative-progress bin; weights normalize over eligible experts for each bin",
+        "router_heatmap_color_scale_min": 0.0,
+        "router_heatmap_color_scale_max": 1.0,
+        "router_heatmap_color_norm": "PowerNorm gamma=0.5; colorbar ticks retain raw alpha values",
+        "conditions": summaries,
+        "controlled_missing_note": "The partial examples are controlled masks, not native Attachment 4 missingness. The complete examples retain all 50 bins in all modalities.",
+        "relative_time_limit": "The unaligned rows have no physical timestamps; frames and waveform are shown against normalized progress only.",
+        "outputs": [png_path.name, pdf_path.name, "mofe_router_heatmap_summary.csv", "mofe_router_heatmap_scores.csv"],
+    }
+    (output_dir / "mofe_router_heatmap_manifest.json").write_text(
+        json.dumps(manifest, ensure_ascii=False, indent=2) + "\n", encoding="utf-8"
+    )
+    print(json.dumps({"output_dir": str(output_dir), "summary": summaries, "files": manifest["outputs"]},
+                     ensure_ascii=False, indent=2))
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--output-dir", type=Path, default=q3.PROJECT_ROOT / "output" / "q3")
+    parser.add_argument("--checkpoint", type=Path, default=q3.DEFAULT_MOFE)
+    parser.add_argument("--scaler", type=Path, default=q3.DEFAULT_SCALER)
+    parser.add_argument("--device", choices=("auto", "cpu", "cuda"), default="auto")
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    create_plot(parse_args())
diff --git a/final/q3/run_experiments.py b/final/q3/run_experiments.py
new file mode 100644
index 0000000..09ddbe5
--- /dev/null
+++ b/final/q3/run_experiments.py
@@ -0,0 +1,1413 @@
+"""Run the first Q3 explanation comparison on the official Attachment 4 cases.
+
+The runner reuses the Q2 EarlyConcat and MoFE checkpoints and their train-only
+robust scaler. E1 and E2 are two explanation views of the same MoFE model.
+"""
+from __future__ import annotations
+
+import argparse
+import csv
+import gc
+import hashlib
+import itertools
+import json
+import math
+import pickle
+import re
+import shutil
+import subprocess
+import time
+from pathlib import Path
+from typing import Any, Iterable, Mapping
+
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+from scipy.stats import spearmanr
+from sklearn.metrics import accuracy_score, f1_score, mean_absolute_error, mean_squared_error
+
+from ..adapter import Q1AlignmentAdapter, adapt_official_split
+from ..data_paths import ATTACHMENT2, ATTACHMENT4, DATA_ROOT, PROJECT_ROOT
+from ..model.early_concat import AlignedFusionModel
+from ..model.mofe import EXPERT_NAMES, MixtureOfFusionExperts
+from ..q2.deep_learning.q2.data import MODALITIES
+
+
+SEED = 20260924
+CLASS_NAMES = ("negative", "neutral", "positive")
+MODEL_VARIANTS = (
+    ("E0_EarlyConcat", "early_concat", "exact Shapley + multiscale occlusion"),
+    ("E1_MoFE_Router", "mofe", "router weights, tested by counterfactual deletion"),
+    ("E2_MoFE_Shapley", "mofe", "exact Shapley + multiscale occlusion"),
+)
+COALITIONS = tuple(
+    frozenset(c)
+    for size in range(4)
+    for c in itertools.combinations(range(3), size)
+)
+WINDOWS = (1, 3, 5)
+EXPLANATION_FRACTION = 0.10
+DEFAULT_RUN = PROJECT_ROOT / "experiments" / "q2" / "unaligned_deep_two_b128"
+DEFAULT_EARLY = DEFAULT_RUN / "models" / "B0_early_concat" / "seed_20260924" / "model_best.pt"
+DEFAULT_MOFE = DEFAULT_RUN / "models" / "B5_mofe_mlp" / "seed_20260924" / "model_best.pt"
+DEFAULT_SCALER = DEFAULT_RUN / "unaligned_50_robust_stats.npz"
+
+try:
+    from transformers import AutoTokenizer
+except ImportError:  # Token highlighting degrades gracefully; inference has no HF dependency.
+    AutoTokenizer = None  # type: ignore[assignment,misc]
+
+
+def _sha256(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as stream:
+        for block in iter(lambda: stream.read(1024 * 1024), b""):
+            digest.update(block)
+    return digest.hexdigest()
+
+
+def _decode(value: Any) -> str:
+    if isinstance(value, bytes):
+        return value.decode("utf-8", errors="replace")
+    if isinstance(value, np.bytes_):
+        return bytes(value).decode("utf-8", errors="replace")
+    if isinstance(value, np.ndarray):
+        if value.shape == ():
+            return _decode(value.item())
+        return " ".join(_decode(x) for x in value.reshape(-1))
+    return str(value)
+
+
+def _safe_name(value: str) -> str:
+    name = re.sub(r"[^A-Za-z0-9_.-]+", "_", value).strip("_.")
+    return name or "sample"
+
+
+def _write_csv(path: Path, rows: list[dict[str, Any]]) -> None:
+    if not rows:
+        return
+    path.parent.mkdir(parents=True, exist_ok=True)
+    fields = list(dict.fromkeys(key for row in rows for key in row))
+    with path.open("w", newline="", encoding="utf-8-sig") as stream:
+        writer = csv.DictWriter(stream, fieldnames=fields, extrasaction="ignore")
+        writer.writeheader()
+        writer.writerows(rows)
+
+
+def _write_json(path: Path, payload: Any) -> None:
+    path.write_text(json.dumps(payload, ensure_ascii=False, indent=2, allow_nan=False) + "\n", encoding="utf-8")
+
+
+def _optional_float(value: Any) -> float | None:
+    value = float(value)
+    return value if math.isfinite(value) else None
+
+
+def exact_shapley(values: Mapping[frozenset[int], float], player_count: int = 3) -> np.ndarray:
+    """Exact Shapley values for a small finite coalition game."""
+    players = set(range(player_count))
+    result = np.zeros(player_count, dtype=np.float64)
+    denom = math.factorial(player_count)
+    for player in range(player_count):
+        others = sorted(players - {player})
+        for size in range(player_count):
+            for coalition_tuple in itertools.combinations(others, size):
+                coalition = frozenset(coalition_tuple)
+                weight = math.factorial(size) * math.factorial(player_count - size - 1) / denom
+                result[player] += weight * (
+                    values[coalition | {player}] - values[coalition]
+                )
+    return result
+
+
+def exact_pair_interactions(
+    values: Mapping[frozenset[int], float], player_count: int = 3
+) -> dict[tuple[int, int], float]:
+    """Shapley interaction index with the standard one-half pair coefficient."""
+    result: dict[tuple[int, int], float] = {}
+    for first, second in itertools.combinations(range(player_count), 2):
+        remaining = sorted(set(range(player_count)) - {first, second})
+        total = 0.0
+        for size in range(len(remaining) + 1):
+            for coalition_tuple in itertools.combinations(remaining, size):
+                coalition = frozenset(coalition_tuple)
+                weight = (
+                    math.factorial(size)
+                    * math.factorial(player_count - size - 2)
+                    / (2 * math.factorial(player_count - 1))
+                )
+                total += weight * (
+                    values[coalition | {first, second}]
+                    - values[coalition | {first}]
+                    - values[coalition | {second}]
+                    + values[coalition]
+                )
+        result[(first, second)] = float(total)
+    return result
+
+
+def _load_scaler(path: Path) -> tuple[tuple[np.ndarray, ...], tuple[np.ndarray, ...]]:
+    with np.load(path, allow_pickle=False) as archive:
+        centers = tuple(archive[f"{name}_center"].astype(np.float32) for name in MODALITIES)
+        scales = tuple(archive[f"{name}_scale"].astype(np.float32) for name in MODALITIES)
+    if any(np.any(~np.isfinite(scale)) or np.any(scale <= 0) for scale in scales):
+        raise ValueError(f"invalid robust scaler: {path}")
+    return centers, scales
+
+
+def _scale_features(
+    features: tuple[np.ndarray, ...],
+    mask: np.ndarray,
+    centers: tuple[np.ndarray, ...],
+    scales: tuple[np.ndarray, ...],
+) -> tuple[np.ndarray, ...]:
+    result: list[np.ndarray] = []
+    for index, source in enumerate(features):
+        values = (np.asarray(source, dtype=np.float32) - centers[index]) / scales[index]
+        values = np.nan_to_num(values, nan=0.0, posinf=0.0, neginf=0.0)
+        visible = mask[:, index] if mask.ndim == 2 else mask[:, :, index]
+        values *= visible[..., None]
+        result.append(values.astype(np.float32, copy=False))
+    return tuple(result)
+
+
+def _attachment4_paths(version: str) -> tuple[Path, Path]:
+    if version != "unaligned_50":
+        raise ValueError("Q3 uses the official unaligned_50 Attachment 4 features")
+    inner = ATTACHMENT4 / "附件4-可解释专项视频样本与特征文件"
+    version_dir = inner / "未对齐版本"
+    # The submitted archive places videos inside the feature-version folder.
+    video_dir = version_dir / "videos"
+    if not video_dir.is_dir():
+        video_dir = inner / "videos"
+    if not version_dir.is_dir():
+        raise FileNotFoundError(f"Attachment 4 feature folder not found: {version_dir}")
+    return version_dir, video_dir
+
+
+def _media_duration(path: Path | None) -> float | None:
+    if path is None or not path.is_file():
+        return None
+    try:
+        proc = subprocess.run(
+            [
+                "ffprobe", "-v", "error", "-show_entries", "format=duration",
+                "-of", "default=noprint_wrappers=1:nokey=1", str(path),
+            ],
+            check=True,
+            capture_output=True,
+            text=True,
+            timeout=20,
+        )
+        duration = float(proc.stdout.strip())
+        return duration if duration > 0 else None
+    except (OSError, subprocess.SubprocessError, ValueError):
+        return None
+
+
+def _read_attachment4(version: str) -> tuple[list[dict[str, Any]], dict[str, str]]:
+    version_dir, video_dir = _attachment4_paths(version)
+    paths = sorted(version_dir.glob("*.pkl"), key=lambda p: p.name)
+    if len(paths) != 20:
+        raise FileNotFoundError(f"expected 20 Attachment 4 cases, found {len(paths)} in {version_dir}")
+    video_by_stem = {p.stem: p for p in video_dir.glob("*.mp4")} if video_dir.is_dir() else {}
+    adapter = Q1AlignmentAdapter(target_steps=50)
+    cases: list[dict[str, Any]] = []
+    for path in paths:
+        with path.open("rb") as stream:
+            raw = pickle.load(stream, encoding="latin1")
+        case_id = _decode(raw.get("id", path.stem)).strip() or path.stem
+        text_bert = np.asarray(raw["text_bert"], dtype=np.int64)
+        if text_bert.ndim == 3 and text_bert.shape[0] == 1:
+            text_bert = text_bert[0]
+        if text_bert.shape != (3, 50):
+            raise ValueError(f"{path.name}: expected text_bert shape (3,50), got {text_bert.shape}")
+        record = {
+            "id": case_id,
+            "sequence_order_verified": True,
+            "attention_mask": text_bert[1].astype(bool),
+            "text": np.asarray(raw["text"], dtype=np.float32),
+            "audio": np.asarray(raw["audio"], dtype=np.float32),
+            "vision": np.asarray(raw["vision"], dtype=np.float32),
+            "audio_length": int(np.asarray(raw["audio_lengths"]).reshape(-1)[0]),
+            "vision_length": int(np.asarray(raw["vision_lengths"]).reshape(-1)[0]),
+        }
+        aligned = adapter.align(record, mode="relative")
+        mask = np.stack([aligned.observed[name] for name in MODALITIES], axis=-1)
+        features = tuple(aligned.features[name].astype(np.float32) for name in MODALITIES)
+        video = video_by_stem.get(path.stem) or video_by_stem.get(case_id)
+        try:
+            video_relative = video.resolve().relative_to(DATA_ROOT).as_posix() if video is not None else ""
+        except ValueError:
+            video_relative = str(video.resolve()) if video is not None else ""
+        duration = _media_duration(video)
+        cases.append(
+            {
+                "case_id": case_id,
+                "source_file": path,
+                "source_sha256": _sha256(path),
+                "transcript": _decode(raw.get("raw_text", "")),
+                "text_bert": text_bert,
+                "features": features,
+                "mask": mask,
+                "target_intervals": aligned.target_intervals.astype(np.float32),
+                "provenance": aligned.provenance,
+                "video_file": video,
+                "video_path": video_relative,
+                "video_duration_sec": duration,
+                "input_audit": {
+                    "case_id": case_id,
+                    "source_file": path.name,
+                    "source_sha256": _sha256(path),
+                    "coordinate_mode": aligned.metadata["coordinate_mode"],
+                    "physical_time_alignment": False,
+                    "audio_reported_length": record["audio_length"],
+                    "vision_reported_length": record["vision_length"],
+                    "audio_length_conflict": bool(aligned.provenance["audio"].length_conflict),
+                    "vision_length_conflict": bool(aligned.provenance["vision"].length_conflict),
+                    "text_visible_target_slots": int(aligned.observed["text"].sum()),
+                    "audio_visible_target_slots": int(aligned.observed["audio"].sum()),
+                    "vision_visible_target_slots": int(aligned.observed["vision"].sum()),
+                    "source_video": video_relative,
+                    "source_video_duration_sec": _optional_float(duration) if video else None,
+                },
+            }
+        )
+    return cases, {"version_dir": str(version_dir), "video_dir": str(video_dir)}
+
+
+def _build_model(kind: str, dims: tuple[int, int, int], checkpoint_path: Path, device: torch.device) -> torch.nn.Module:
+    checkpoint = torch.load(checkpoint_path, map_location="cpu", weights_only=True)
+    stored_dims = tuple(int(x) for x in checkpoint.get("dims", ()))
+    if stored_dims != dims:
+        raise ValueError(f"{checkpoint_path} expects {stored_dims}, adapter produced {dims}")
+    if kind == "early_concat":
+        model: torch.nn.Module = AlignedFusionModel("concat", dims=dims)
+    elif kind == "mofe":
+        model = MixtureOfFusionExperts(dims=dims)
+    else:
+        raise ValueError(f"unknown model kind: {kind}")
+    model.load_state_dict(checkpoint["state_dict"], strict=True)
+    model.to(device).eval()
+    return model
+
+
+def _model_output(
+    model: torch.nn.Module,
+    xs: tuple[torch.Tensor, ...],
+    mask: torch.Tensor,
+) -> dict[str, torch.Tensor]:
+    model.eval()
+    with torch.inference_mode():
+        return model(xs, mask)
+
+
+def _batched_output(
+    model: torch.nn.Module,
+    features: tuple[np.ndarray, ...],
+    masks: np.ndarray,
+    device: torch.device,
+    batch_size: int,
+) -> tuple[np.ndarray, np.ndarray]:
+    logits: list[np.ndarray] = []
+    intensity: list[np.ndarray] = []
+    model.eval()
+    with torch.inference_mode():
+        for start in range(0, len(masks), batch_size):
+            end = min(start + batch_size, len(masks))
+            xs = tuple(torch.as_tensor(x[start:end], dtype=torch.float32, device=device) for x in features)
+            batch_mask = torch.as_tensor(masks[start:end], dtype=torch.bool, device=device)
+            output = model(xs, batch_mask)
+            logits.append(output["logits"].float().cpu().numpy())
+            intensity.append(output["intensity"].float().cpu().numpy())
+    return np.concatenate(logits), np.concatenate(intensity)
+
+
+def _single_input_mask_outputs(
+    model: torch.nn.Module,
+    features: tuple[np.ndarray, ...],
+    masks: np.ndarray,
+    device: torch.device,
+    batch_size: int,
+) -> tuple[np.ndarray, np.ndarray]:
+    logits: list[np.ndarray] = []
+    intensity: list[np.ndarray] = []
+    model.eval()
+    with torch.inference_mode():
+        for start in range(0, len(masks), batch_size):
+            end = min(start + batch_size, len(masks))
+            count = end - start
+            xs = tuple(
+                torch.as_tensor(np.repeat(x[None], count, axis=0), dtype=torch.float32, device=device)
+                for x in features
+            )
+            output = model(xs, torch.as_tensor(masks[start:end], dtype=torch.bool, device=device))
+            logits.append(output["logits"].float().cpu().numpy())
+            intensity.append(output["intensity"].float().cpu().numpy())
+    return np.concatenate(logits), np.concatenate(intensity).reshape(-1)
+
+
+def _coalition_outputs(
+    model: torch.nn.Module,
+    features: tuple[np.ndarray, ...],
+    base_mask: np.ndarray,
+    device: torch.device,
+) -> tuple[np.ndarray, np.ndarray]:
+    masks = np.repeat(base_mask[None, :, :], len(COALITIONS), axis=0)
+    for index, coalition in enumerate(COALITIONS):
+        for modality in range(3):
+            if modality not in coalition:
+                masks[index, :, modality] = False
+    xs = tuple(
+        torch.as_tensor(np.repeat(x[None, :, :], len(COALITIONS), axis=0), dtype=torch.float32, device=device)
+        for x in features
+    )
+    with torch.inference_mode():
+        output = model(xs, torch.as_tensor(masks, dtype=torch.bool, device=device))
+    return output["logits"].float().cpu().numpy(), output["intensity"].float().cpu().numpy().reshape(-1)
+
+
+def _values_for_task(
+    coalition_logits: np.ndarray,
+    coalition_intensity: np.ndarray,
+    predicted_class: int,
+) -> tuple[dict[frozenset[int], float], dict[frozenset[int], float]]:
+    class_values = {coalition: float(coalition_logits[i, predicted_class]) for i, coalition in enumerate(COALITIONS)}
+    reg_values = {coalition: float(coalition_intensity[i]) for i, coalition in enumerate(COALITIONS)}
+    return class_values, reg_values
+
+
+def _shares(values: np.ndarray) -> np.ndarray:
+    denominator = float(np.abs(values).sum())
+    return np.abs(values) / denominator if denominator > 1e-12 else np.zeros_like(values)
+
+
+def _source_rows(case: dict[str, Any], modality_index: int, slots: Iterable[int]) -> list[int]:
+    provenance = case["provenance"][MODALITIES[modality_index]]
+    rows: set[int] = set()
+    for slot in slots:
+        rows.update(int(x) for x in provenance.source_weights.getrow(int(slot)).indices)
+    return sorted(rows)
+
+
+def _decode_text_rows(case: dict[str, Any], source_rows: list[int], tokenizer: Any) -> tuple[str, str]:
+    ids = np.asarray(case["text_bert"][0], dtype=np.int64)
+    usable = [index for index in source_rows if 0 <= index < len(ids)]
+    if not usable:
+        return case["transcript"], "whole_transcript_no_token_overlap"
+    if tokenizer is None:
+        return case["transcript"], "whole_transcript_tokenizer_unavailable"
+    selected = [int(ids[index]) for index in usable if int(ids[index]) not in tokenizer.all_special_ids]
+    if not selected:
+        return case["transcript"], "whole_transcript_special_tokens_only"
+    return tokenizer.decode(selected, skip_special_tokens=True, clean_up_tokenization_spaces=True), "bert_token_ids"
+
+
+def _local_occlusion(
+    model: torch.nn.Module,
+    features: tuple[np.ndarray, ...],
+    base_mask: np.ndarray,
+    full_logit: float,
+    full_intensity: float,
+    predicted_class: int,
+    device: torch.device,
+    batch_size: int,
+) -> tuple[dict[tuple[int, int], dict[str, float]], dict[int, np.ndarray]]:
+    masks: list[np.ndarray] = []
+    meta: list[tuple[int, int, int]] = []
+    for modality in range(3):
+        for width in WINDOWS:
+            radius = width // 2
+            for slot in np.flatnonzero(base_mask[:, modality]).tolist():
+                masked = base_mask.copy()
+                left, right = max(0, slot - radius), min(base_mask.shape[0], slot + radius + 1)
+                masked[left:right, modality] = False
+                masks.append(masked)
+                meta.append((modality, slot, width))
+    class_drop: dict[tuple[int, int, int], float] = {}
+    reg_drop: dict[tuple[int, int, int], float] = {}
+    if masks:
+        logits, intensity = _single_input_mask_outputs(model, features, np.stack(masks), device, batch_size)
+        for key, logit_row, regression in zip(meta, logits, intensity):
+            class_drop[key] = float(full_logit - logit_row[predicted_class])
+            reg_drop[key] = float(full_intensity - regression)
+    local: dict[tuple[int, int], dict[str, float]] = {}
+    scale_maps = {width: np.full((3, base_mask.shape[0]), np.nan, dtype=np.float32) for width in WINDOWS}
+    for modality in range(3):
+        for slot in np.flatnonzero(base_mask[:, modality]).tolist():
+            row: dict[str, float] = {}
+            for width in WINDOWS:
+                row[f"class_logit_drop_w{width}"] = class_drop.get((modality, slot, width), 0.0)
+                row[f"intensity_drop_w{width}"] = reg_drop.get((modality, slot, width), 0.0)
+                scale_maps[width][modality, slot] = row[f"class_logit_drop_w{width}"]
+            row["class_logit_drop_multiscale"] = float(np.mean([row[f"class_logit_drop_w{w}"] for w in WINDOWS]))
+            row["intensity_drop_multiscale"] = float(np.mean([row[f"intensity_drop_w{w}"] for w in WINDOWS]))
+            local[(modality, slot)] = row
+    return local, scale_maps
+
+
+def _router_profile(model: torch.nn.Module, features: tuple[np.ndarray, ...], mask: np.ndarray, device: torch.device) -> tuple[dict[str, Any], np.ndarray]:
+    xs = tuple(torch.as_tensor(x[None], dtype=torch.float32, device=device) for x in features)
+    tensor_mask = torch.as_tensor(mask[None], dtype=torch.bool, device=device)
+    output = _model_output(model, xs, tensor_mask)
+    alpha = output.get("alpha")
+    utility = output.get("utility")
+    if alpha is None or utility is None:
+        raise TypeError("router profile requested for a model without MoFE router outputs")
+    alpha_np = alpha[0].float().cpu().numpy()
+    utility_np = utility[0].float().cpu().numpy()
+    valid_slots = mask.any(axis=-1)
+    if valid_slots.any():
+        expert_mean = alpha_np[valid_slots].mean(axis=0)
+        exposure = utility_np[valid_slots].mean(axis=0)
+    else:
+        expert_mean = np.zeros(len(EXPERT_NAMES), dtype=np.float32)
+        exposure = np.zeros(3, dtype=np.float32)
+    exposure_share = _shares(exposure)
+    row: dict[str, Any] = {}
+    for name, value in zip(EXPERT_NAMES, expert_mean):
+        row[f"router_expert_{name}"] = float(value)
+    for index, name in enumerate(MODALITIES):
+        row[f"router_{name}_exposure"] = float(exposure[index])
+        row[f"router_{name}_share"] = float(exposure_share[index])
+    return row, utility_np.T
+
+
+def _spearman(x: np.ndarray, y: np.ndarray) -> float | None:
+    if len(x) < 2 or np.allclose(x, x[0]) or np.allclose(y, y[0]):
+        return None
+    return _optional_float(spearmanr(x, y).statistic)
+
+
+def _scale_stability(scale_maps: dict[int, np.ndarray], mask: np.ndarray) -> dict[str, float | None]:
+    results: dict[str, float | None] = {}
+    values = [scale_maps[width][mask.T] for width in WINDOWS]
+    for (left, right), a, b in zip(((1, 3), (1, 5), (3, 5)), (values[0], values[0], values[1]), (values[1], values[2], values[2])):
+        results[f"spearman_w{left}_w{right}"] = _spearman(a, b)
+    finite = [value for value in results.values() if value is not None]
+    results["mean_scale_spearman"] = float(np.mean(finite)) if finite else None
+    return results
+
+
+def _ranked_positions(profile: np.ndarray, mask: np.ndarray) -> list[tuple[int, int]]:
+    candidates = [
+        (modality, slot, float(profile[modality, slot]))
+        for modality in range(3)
+        for slot in range(mask.shape[0])
+        if mask[slot, modality] and math.isfinite(float(profile[modality, slot]))
+    ]
+    return [(m, t) for m, t, _ in sorted(candidates, key=lambda item: (-item[2], item[0], item[1]))]
+
+
+def _faithfulness(
+    model: torch.nn.Module,
+    features: tuple[np.ndarray, ...],
+    base_mask: np.ndarray,
+    full_logit: float,
+    predicted_class: int,
+    importance: np.ndarray,
+    device: torch.device,
+) -> dict[str, Any]:
+    positions = _ranked_positions(importance, base_mask)
+    n = len(positions)
+    deletion_fractions = (0.0, 0.1, 0.2, 0.3, 0.5, 0.7)
+    masks: list[np.ndarray] = []
+    tags: list[tuple[str, float, int]] = []
+    for fraction in deletion_fractions:
+        count = min(n, int(math.ceil(n * fraction))) if fraction else 0
+        masked = base_mask.copy()
+        for modality, slot in positions[:count]:
+            masked[slot, modality] = False
+        masks.append(masked)
+        tags.append(("delete", fraction, count))
+    for fraction in (0.1, 0.2, 0.3):
+        count = min(n, max(1, int(math.ceil(n * fraction)))) if n else 0
+        retained = np.zeros_like(base_mask)
+        for modality, slot in positions[:count]:
+            retained[slot, modality] = True
+        masks.append(retained)
+        tags.append(("retain", fraction, count))
+    logits, _ = _single_input_mask_outputs(model, features, np.stack(masks), device, batch_size=32)
+    row: dict[str, Any] = {"observed_cells": n, "ranking_cells": n}
+    drops: list[float] = []
+    for tag, logit_row in zip(tags, logits):
+        operation, fraction, count = tag
+        score = float(logit_row[predicted_class])
+        change = float(full_logit - score)
+        if operation == "delete":
+            row[f"comprehensiveness_delete_{int(fraction * 100)}pct"] = change
+            row[f"deleted_cells_{int(fraction * 100)}pct"] = count
+            drops.append(change)
+        else:
+            row[f"sufficiency_gap_retain_{int(fraction * 100)}pct"] = change
+            row[f"sufficiency_abs_gap_retain_{int(fraction * 100)}pct"] = abs(change)
+            row[f"retained_cells_{int(fraction * 100)}pct"] = count
+    row["deletion_auc_0_70_mean_logit_drop"] = float(
+        np.trapezoid(np.asarray(drops, dtype=np.float64), x=np.asarray(deletion_fractions)) / 0.7
+    )
+    return row
+
+
+def _segment_profile(
+    profile: np.ndarray,
+    mask: np.ndarray,
+    case: dict[str, Any],
+    variant: str,
+    tokenizer: Any,
+    output_dir: Path,
+    extract_frames: bool,
+) -> list[dict[str, Any]]:
+    segments: list[dict[str, Any]] = []
+    for modality in range(3):
+        visible = np.flatnonzero(mask[:, modality])
+        if not len(visible):
+            continue
+        k = max(1, int(math.ceil(len(visible) * EXPLANATION_FRACTION)))
+        chosen = sorted(
+            visible.tolist(),
+            key=lambda slot: (-abs(float(profile[modality, slot])), slot),
+        )[:k]
+        groups: list[list[int]] = []
+        for slot in sorted(chosen):
+            if groups and slot == groups[-1][-1] + 1:
+                groups[-1].append(slot)
+            else:
+                groups.append([slot])
+        groups.sort(key=lambda group: (-sum(abs(float(profile[modality, t])) for t in group), group[0]))
+        for rank, group in enumerate(groups[:2], start=1):
+            rows = _source_rows(case, modality, group)
+            start = float(case["target_intervals"][group[0], 0])
+            end = float(case["target_intervals"][group[-1], 1])
+            duration = case["video_duration_sec"]
+            start_sec = start * duration if duration is not None else None
+            end_sec = end * duration if duration is not None else None
+            if modality == 0:
+                evidence, text_method = _decode_text_rows(case, rows, tokenizer)
+            elif modality == 1:
+                evidence = f"unaligned audio feature rows {min(rows) if rows else 0}–{max(rows) if rows else -1}; review the linked source clip at the estimated relative span"
+                text_method = "feature_row_provenance"
+            else:
+                evidence = f"unaligned vision feature rows {min(rows) if rows else 0}–{max(rows) if rows else -1}; candidate frame time is estimated from relative progress"
+                text_method = "feature_row_provenance"
+            signed = float(sum(float(profile[modality, t]) for t in group))
+            strength = float(sum(abs(float(profile[modality, t])) for t in group))
+            if variant == "E1_MoFE_Router":
+                direction = "router_activity_not_signed_contribution"
+                score_semantics = "internal router utility; not a prediction effect"
+            else:
+                direction = "supports_predicted_class" if signed > 0 else ("opposes_predicted_class" if signed < 0 else "neutral")
+                score_semantics = "predicted-class logit drop after local occlusion"
+            frame_path = ""
+            if modality == 2 and extract_frames and case["video_file"] is not None and start_sec is not None and end_sec is not None:
+                midpoint = (start_sec + end_sec) / 2.0
+                if duration:
+                    midpoint = min(max(midpoint, 0.0), max(0.0, duration - 0.05))
+                name = f"{_safe_name(variant)}_{_safe_name(case['case_id'])}_vision_{rank}.jpg"
+                target = output_dir / "evidence_frames" / name
+                target.parent.mkdir(parents=True, exist_ok=True)
+                try:
+                    subprocess.run(
+                        ["ffmpeg", "-hide_banner", "-loglevel", "error", "-y", "-ss", f"{midpoint:.4f}",
+                         "-i", str(case["video_file"]), "-frames:v", "1", "-vf", "scale=640:-2", str(target)],
+                        check=True,
+                        capture_output=True,
+                        timeout=30,
+                    )
+                    frame_path = target.relative_to(output_dir).as_posix()
+                except (OSError, subprocess.SubprocessError):
+                    frame_path = ""
+            segments.append(
+                {
+                    "variant": variant,
+                    "case_id": case["case_id"],
+                    "modality": MODALITIES[modality],
+                    "rank_within_modality": rank,
+                    "slot_start_0based": group[0],
+                    "slot_end_exclusive": group[-1] + 1,
+                    "relative_progress_start": start,
+                    "relative_progress_end": end,
+                    "source_row_start": min(rows) if rows else 0,
+                    "source_row_end_exclusive": max(rows) + 1 if rows else 0,
+                    "local_score_sum_signed": signed,
+                    "local_score_mass": strength,
+                    "direction": direction,
+                    "score_semantics": score_semantics,
+                    "evidence_text": evidence,
+                    "text_evidence_method": text_method,
+                    "source_video": case["video_path"],
+                    "video_time_start_sec_estimate": start_sec,
+                    "video_time_end_sec_estimate": end_sec,
+                    "video_time_basis": "relative progress times clip duration; approximate, not a physical feature timestamp",
+                    "candidate_frame": frame_path,
+                }
+            )
+    return segments
+
+
+def _plot_profile(
+    path: Path,
+    profile: np.ndarray,
+    mask: np.ndarray,
+    title: str,
+    router: bool = False,
+) -> None:
+    values = np.asarray(profile, dtype=np.float32).copy()
+    values[~mask.T] = np.nan
+    fig, ax = plt.subplots(figsize=(11, 2.8), constrained_layout=True)
+    cmap = plt.get_cmap("viridis" if router else "coolwarm").copy()
+    cmap.set_bad("#d8d8d8")
+    if router:
+        vmax = max(float(np.nanmax(values)) if np.isfinite(values).any() else 0.0, 1e-6)
+        image = ax.imshow(np.ma.masked_invalid(values), aspect="auto", interpolation="nearest", cmap=cmap, vmin=0, vmax=vmax)
+    else:
+        bound = max(float(np.nanmax(np.abs(values))) if np.isfinite(values).any() else 0.0, 1e-6)
+        image = ax.imshow(np.ma.masked_invalid(values), aspect="auto", interpolation="nearest", cmap=cmap, vmin=-bound, vmax=bound)
+    ax.set_yticks(range(3), ("Text", "Audio", "Vision"))
+    ax.set_xticks(range(0, 50, 5), range(0, 50, 5))
+    ax.set_xlabel("Relative progress bin (0-based)")
+    ax.set_title(title)
+    fig.colorbar(image, ax=ax, label="router utility" if router else "predicted-class logit drop")
+    path.parent.mkdir(parents=True, exist_ok=True)
+    fig.savefig(path, dpi=150)
+    plt.close(fig)
+
+
+def _metrics(y_cls: np.ndarray, y_reg: np.ndarray, logits: np.ndarray, intensity: np.ndarray) -> dict[str, Any]:
+    predicted = logits.argmax(axis=-1)
+    score = np.clip(intensity.reshape(-1), -3.0, 3.0)
+    pearson = float(np.corrcoef(y_reg, score)[0, 1]) if np.std(y_reg) > 0 and np.std(score) > 0 else None
+    return {
+        "n": int(len(y_cls)),
+        "accuracy": float(accuracy_score(y_cls, predicted)),
+        "macro_f1": float(f1_score(y_cls, predicted, labels=[0, 1, 2], average="macro", zero_division=0)),
+        "mae": float(mean_absolute_error(y_reg, score)),
+        "rmse": float(math.sqrt(mean_squared_error(y_reg, score))),
+        "pearson": pearson,
+    }
+
+
+def _load_validation(
+    path: Path,
+    centers: tuple[np.ndarray, ...],
+    scales: tuple[np.ndarray, ...],
+) -> tuple[list[str], tuple[np.ndarray, ...], np.ndarray, np.ndarray, np.ndarray, dict[str, Any]]:
+    if not path.is_file():
+        raise FileNotFoundError(f"Attachment 2 unaligned_50 pickle not found: {path}")
+    print(f"Loading the official validation split from {path} ...", flush=True)
+    with path.open("rb") as stream:
+        raw = pickle.load(stream, encoding="latin1")
+    part = raw["valid"]
+    raw_ids = part["id"]
+    ids = [_decode(value) for value in raw_ids]
+    y_cls = np.asarray(part["classification_labels"], dtype=np.int64).reshape(-1)
+    y_reg = np.asarray(part["regression_labels"], dtype=np.float32).reshape(-1)
+    features_dict, mask, audit = adapt_official_split(part)
+    features = tuple(features_dict[name] for name in MODALITIES)
+    del part, raw, raw_ids, features_dict
+    gc.collect()
+    features = _scale_features(features, mask, centers, scales)
+    return ids, features, mask, y_cls, y_reg, audit
+
+
+def _validation_errors(
+    ids: list[str],
+    features: tuple[np.ndarray, ...],
+    mask: np.ndarray,
+    y_cls: np.ndarray,
+    y_reg: np.ndarray,
+    model_rows: dict[str, tuple[torch.nn.Module, np.ndarray, np.ndarray]],
+    device: torch.device,
+    batch_size: int,
+    output_dir: Path,
+) -> tuple[dict[str, Any], int]:
+    predictions: list[dict[str, Any]] = []
+    error_indices: set[int] = set()
+    validation_metrics: dict[str, Any] = {}
+    cache: dict[str, tuple[np.ndarray, np.ndarray]] = {}
+    for model_name, (model, _unused_logits, _unused_intensity) in model_rows.items():
+        logits, intensity = _batched_output(model, features, mask, device, batch_size)
+        cache[model_name] = logits, intensity
+        validation_metrics[model_name] = _metrics(y_cls, y_reg, logits, intensity)
+        predicted = logits.argmax(axis=-1)
+        abs_error = np.abs(y_reg - np.clip(intensity, -3.0, 3.0))
+        misses = np.flatnonzero(predicted != y_cls)
+        error_indices.update(int(i) for i in misses)
+        top_reg = np.argsort(-abs_error)[: min(20, len(abs_error))]
+        error_indices.update(int(i) for i in top_reg)
+        for i, sample_id in enumerate(ids):
+            probabilities = torch.softmax(torch.as_tensor(logits[i]), dim=-1).numpy()
+            predictions.append(
+                {
+                    "model": model_name,
+                    "sample_id": sample_id,
+                    "true_class": int(y_cls[i]),
+                    "true_class_name": CLASS_NAMES[int(y_cls[i])],
+                    "predicted_class": int(predicted[i]),
+                    "predicted_class_name": CLASS_NAMES[int(predicted[i])],
+                    "classification_correct": bool(predicted[i] == y_cls[i]),
+                    "true_intensity": float(y_reg[i]),
+                    "predicted_intensity": float(intensity[i]),
+                    "absolute_intensity_error": float(abs_error[i]),
+                    "p_negative": float(probabilities[0]),
+                    "p_neutral": float(probabilities[1]),
+                    "p_positive": float(probabilities[2]),
+                }
+            )
+    _write_csv(output_dir / "validation_predictions.csv", predictions)
+    _write_json(output_dir / "validation_metrics.json", validation_metrics)
+
+    sorted_errors = sorted(error_indices, key=lambda i: (y_cls[i] == cache["early_concat"][0][i].argmax(), -abs(float(y_reg[i] - cache["early_concat"][1][i]))))
+    error_rows: list[dict[str, Any]] = []
+    for i in sorted_errors:
+        for model_name, (logits, intensity) in cache.items():
+            predicted = int(logits[i].argmax())
+            error_rows.append(
+                {
+                    "model": model_name,
+                    "sample_id": ids[i],
+                    "true_class_name": CLASS_NAMES[int(y_cls[i])],
+                    "predicted_class_name": CLASS_NAMES[predicted],
+                    "classification_correct": bool(predicted == y_cls[i]),
+                    "true_intensity": float(y_reg[i]),
+                    "predicted_intensity": float(intensity[i]),
+                    "absolute_intensity_error": float(abs(float(y_reg[i] - intensity[i]))),
+                    "error_selection": "classification error or top-20 intensity error",
+                }
+            )
+    _write_csv(output_dir / "validation_errors.csv", error_rows)
+
+    attribution: list[dict[str, Any]] = []
+    for i in sorted_errors[:50]:
+        for model_name, (model, _, _) in model_rows.items():
+            coal_logits, coal_intensity = _coalition_outputs(model, tuple(x[i] for x in features), mask[i], device)
+            predicted = int(cache[model_name][0][i].argmax())
+            true = int(y_cls[i])
+            if predicted != true:
+                alternative = predicted
+                target_name = "predicted_logit_minus_true_logit"
+                class_values = {
+                    coalition: float(coal_logits[j, alternative] - coal_logits[j, true])
+                    for j, coalition in enumerate(COALITIONS)
+                }
+            else:
+                alternatives = [c for c in range(3) if c != true]
+                alternative = max(alternatives, key=lambda c: float(cache[model_name][0][i, c]))
+                target_name = "true_logit_minus_best_alternative"
+                class_values = {
+                    coalition: float(coal_logits[j, true] - coal_logits[j, alternative])
+                    for j, coalition in enumerate(COALITIONS)
+                }
+            reg_values = {coalition: float(coal_intensity[j]) for j, coalition in enumerate(COALITIONS)}
+            class_phi = exact_shapley(class_values)
+            reg_phi = exact_shapley(reg_values)
+            row: dict[str, Any] = {
+                "model": model_name,
+                "sample_id": ids[i],
+                "true_class_name": CLASS_NAMES[true],
+                "predicted_class_name": CLASS_NAMES[predicted],
+                "classification_correct": bool(predicted == true),
+                "true_intensity": float(y_reg[i]),
+                "predicted_intensity": float(cache[model_name][1][i]),
+                "absolute_intensity_error": float(abs(float(y_reg[i] - cache[model_name][1][i]))),
+                "classification_margin_target": target_name,
+                "classification_margin_phi_sum_residual": float(class_phi.sum() - (class_values[COALITIONS[-1]] - class_values[frozenset()])),
+                "regression_shapley_sum_residual": float(reg_phi.sum() - (reg_values[COALITIONS[-1]] - reg_values[frozenset()])),
+            }
+            for m, name in enumerate(MODALITIES):
+                row[f"class_margin_phi_{name}"] = float(class_phi[m])
+                row[f"class_margin_share_{name}"] = float(_shares(class_phi)[m])
+                row[f"regression_phi_{name}"] = float(reg_phi[m])
+                row[f"regression_share_{name}"] = float(_shares(reg_phi)[m])
+            row["class_margin_dominant_modality"] = MODALITIES[int(np.argmax(np.abs(class_phi)))]
+            attribution.append(row)
+    _write_csv(output_dir / "validation_error_attribution.csv", attribution)
+    return validation_metrics, len(sorted_errors)
+
+
+def _q2_reference_metrics(path: Path) -> dict[str, dict[str, float]]:
+    if not path.is_file():
+        return {}
+    rows: dict[str, dict[str, float]] = {}
+    with path.open("r", newline="", encoding="utf-8-sig") as stream:
+        for row in csv.DictReader(stream):
+            if row.get("split") != "official_valid":
+                continue
+            if row.get("model") not in ("EarlyConcat", "MoFE-7"):
+                continue
+            rows[row["model"]] = {
+                key: float(row[key]) for key in ("accuracy", "macro_f1", "mae", "rmse", "pearson")
+            }
+    return rows
+
+
+def _make_cards(
+    output_dir: Path,
+    cases: list[dict[str, Any]],
+    results: list[dict[str, Any]],
+    shapley_by_key: dict[tuple[str, str], dict[str, Any]],
+    interaction_by_key: dict[tuple[str, str], dict[str, Any]],
+    router_by_id: dict[str, dict[str, Any]],
+    segment_rows: list[dict[str, Any]],
+    faithfulness_rows: list[dict[str, Any]],
+) -> str:
+    case_by_id = {case["case_id"]: case for case in cases}
+    segments_by: dict[tuple[str, str], list[dict[str, Any]]] = {}
+    for row in segment_rows:
+        segments_by.setdefault((str(row["variant"]), str(row["case_id"])), []).append(row)
+    faith_by: dict[tuple[str, str], dict[str, Any]] = {
+        (str(row["variant"]), str(row["case_id"])): row for row in faithfulness_rows
+    }
+    result_by = {(str(row["variant"]), str(row["case_id"])): row for row in results}
+    card_paths: dict[tuple[str, str], Path] = {}
+    confidences = [float(row["confidence"]) for row in results if row["variant"] == "E2_MoFE_Shapley"]
+    median_conf = float(np.median(confidences))
+    typical_id = min(
+        (str(row["case_id"]) for row in results if row["variant"] == "E2_MoFE_Shapley"),
+        key=lambda cid: abs(result_by[("E2_MoFE_Shapley", cid)]["confidence"] - median_conf),
+    )
+    for variant, _, _ in MODEL_VARIANTS:
+        folder = output_dir / "explanation_cards" / variant
+        folder.mkdir(parents=True, exist_ok=True)
+        for case in cases:
+            cid = str(case["case_id"])
+            item = result_by[(variant, cid)]
+            base_model = "early_concat" if variant == "E0_EarlyConcat" else "mofe"
+            shap = shapley_by_key[(base_model, cid)]
+            inter = interaction_by_key[(base_model, cid)]
+            router = router_by_id.get(cid, {})
+            faithful = faith_by[(variant, cid)]
+            lines = [
+                f"# Q3 explanation card — {variant} — {cid}",
+                "",
+                "## Prediction",
+                "",
+                f"- Polarity: **{item['predicted_class_name']}**",
+                f"- Intensity: {item['predicted_sentiment']:+.3f}",
+                f"- Predicted-class confidence: {item['confidence']:.3f}",
+                f"- Source clip: {case['video_path'] or 'video not found'}",
+                "",
+            ]
+            if variant == "E1_MoFE_Router":
+                lines += [
+                    "## Router profile (intrinsic routing signal, not prediction contribution)",
+                    "",
+                    "| Modality | Router exposure share | Exact Shapley absolute share |",
+                    "|---|---:|---:|",
+                ]
+                for name in MODALITIES:
+                    lines.append(
+                        f"| {name} | {router.get(f'router_{name}_share', 0.0):.3f} | {shap[f'class_share_{name}']:.3f} |"
+                    )
+                lines += [
+                    "",
+                    f"Router–Shapley Spearman: {router.get('router_shapley_spearman')}; top modality agreement: {router.get('router_shapley_top1_agreement')}.",
+                    "Router values describe mixture routing. The counterfactual scores below test whether that routing signal tracks model behavior.",
+                    "",
+                ]
+            else:
+                lines += [
+                    "## Exact modality Shapley",
+                    "",
+                    "Positive values support the predicted class logit; negative values oppose it. Shares use absolute values and are model decision contributions, not real-world emotion importance.",
+                    "",
+                    "| Modality | Class logit contribution | Absolute share | Intensity contribution | Absolute share |",
+                    "|---|---:|---:|---:|---:|",
+                ]
+                for name in MODALITIES:
+                    lines.append(
+                        f"| {name} | {shap[f'class_phi_{name}']:+.4f} | {shap[f'class_share_{name}']:.3f} | {shap[f'regression_phi_{name}']:+.4f} | {shap[f'regression_share_{name}']:.3f} |"
+                    )
+                lines += [
+                    "",
+                    f"Shapley completeness residuals: class {shap['class_completeness_residual']:.2e}, intensity {shap['regression_completeness_residual']:.2e}.",
+                    "## Pairwise Shapley interaction",
+                    "",
+                    "| Pair | Class logit | Intensity |",
+                    "|---|---:|---:|",
+                    f"| Text + Audio | {inter['class_interaction_TA']:+.4f} | {inter['regression_interaction_TA']:+.4f} |",
+                    f"| Text + Vision | {inter['class_interaction_TV']:+.4f} | {inter['regression_interaction_TV']:+.4f} |",
+                    f"| Audio + Vision | {inter['class_interaction_AV']:+.4f} | {inter['regression_interaction_AV']:+.4f} |",
+                    "",
+                ]
+            lines += [
+                "## Local evidence segments",
+                "",
+                "Local counterfactual scores are the predicted-class logit difference after hiding a 1/3/5-bin window, averaged equally across the three scales. E1 ranks its router utility and is evaluated separately.",
+                "",
+            ]
+            evidence = segments_by.get((variant, cid), [])
+            for row in evidence:
+                segment = (
+                    f"{row['modality']} bins {row['slot_start_0based']}–{int(row['slot_end_exclusive']) - 1} "
+                    f"(relative progress {row['relative_progress_start']:.3f}–{row['relative_progress_end']:.3f})"
+                )
+                if row.get("video_time_start_sec_estimate") is not None:
+                    segment += f", estimated clip interval {row['video_time_start_sec_estimate']:.2f}–{row['video_time_end_sec_estimate']:.2f}s"
+                lines.append(f"- **{segment}** — {row['direction']}; evidence: {row['evidence_text']}")
+                if row.get("candidate_frame"):
+                    lines.append(f"  - Candidate frame: ![estimated visual evidence](../../{row['candidate_frame']})")
+            if not evidence:
+                lines.append("- No observed local evidence cells for this sample.")
+            image_rel = Path("..") / ".." / "evidence_profiles" / variant / f"{_safe_name(cid)}.png"
+            lines += [
+                "",
+                f"![Local evidence profile]({image_rel.as_posix()})",
+                "",
+                "## Faithfulness checks",
+                "",
+                f"- Comprehensiveness after deleting the top 10% / 30% cells: {faithful['comprehensiveness_delete_10pct']:+.4f} / {faithful['comprehensiveness_delete_30pct']:+.4f} predicted-class logit.",
+                f"- Sufficiency gap when retaining the top 10% / 30%: {faithful['sufficiency_gap_retain_10pct']:+.4f} / {faithful['sufficiency_gap_retain_30pct']:+.4f}. Smaller absolute gaps are better.",
+                f"- Mean deletion logit drop over 0–70% deletion: {faithful['deletion_auc_0_70_mean_logit_drop']:+.4f}.",
+                "",
+                "## Provenance limit",
+                "",
+                "Attachment 4 supplies unaligned feature sequences without word/audio/frame timestamps. Feature rows are traced to source rows and normalized progress. Clip-time estimates multiply that progress by the video duration; they are approximate review locations, not physical alignment timestamps.",
+                "",
+                "Occlusion and Shapley values describe this trained model's response to masked inputs. They do not establish causal effects or prove the emotion expressed by a person.",
+                "",
+                "## Transcript",
+                "",
+                case["transcript"] or "(not supplied)",
+                "",
+            ]
+            path = folder / f"{_safe_name(cid)}.md"
+            path.write_text("\n".join(lines), encoding="utf-8")
+            card_paths[(variant, cid)] = path
+    source = card_paths[("E2_MoFE_Shapley", typical_id)]
+    (output_dir / "typical_explanation_card.md").write_text(source.read_text(encoding="utf-8"), encoding="utf-8")
+    return typical_id
+
+
+def _evaluate_case(
+    case: dict[str, Any],
+    models: dict[str, torch.nn.Module],
+    device: torch.device,
+    explanation_batch_size: int,
+    tokenizer: Any,
+    output_dir: Path,
+    extract_frames: bool,
+) -> dict[str, Any]:
+    xs = case["features"]
+    mask = case["mask"]
+    result: dict[str, Any] = {"case_id": case["case_id"], "models": {}, "variant_rows": []}
+    local_by_model: dict[str, dict[tuple[int, int], dict[str, float]]] = {}
+    scale_by_model: dict[str, dict[int, np.ndarray]] = {}
+    router_by_model: dict[str, dict[str, Any]] = {}
+    for model_name, model in models.items():
+        outputs = _model_output(
+            model,
+            tuple(torch.as_tensor(x[None], dtype=torch.float32, device=device) for x in xs),
+            torch.as_tensor(mask[None], dtype=torch.bool, device=device),
+        )
+        logits = outputs["logits"][0].float().cpu().numpy()
+        intensity = float(outputs["intensity"][0].float().cpu().item())
+        probabilities = torch.softmax(outputs["logits"][0].float(), dim=-1).cpu().numpy()
+        predicted = int(logits.argmax())
+        class_values, reg_values = _values_for_task(
+            *_coalition_outputs(model, xs, mask, device),
+            predicted,
+        )
+        class_phi = exact_shapley(class_values)
+        reg_phi = exact_shapley(reg_values)
+        class_share = _shares(class_phi)
+        reg_share = _shares(reg_phi)
+        class_interactions = exact_pair_interactions(class_values)
+        reg_interactions = exact_pair_interactions(reg_values)
+        modal_row: dict[str, Any] = {
+            "base_model": model_name,
+            "case_id": case["case_id"],
+            "predicted_class_name": CLASS_NAMES[predicted],
+            "class_value_empty": class_values[frozenset()],
+            "class_value_full": class_values[COALITIONS[-1]],
+            "regression_value_empty": reg_values[frozenset()],
+            "regression_value_full": reg_values[COALITIONS[-1]],
+            "class_completeness_residual": float(class_phi.sum() - (class_values[COALITIONS[-1]] - class_values[frozenset()])),
+            "regression_completeness_residual": float(reg_phi.sum() - (reg_values[COALITIONS[-1]] - reg_values[frozenset()])),
+        }
+        for index, name in enumerate(MODALITIES):
+            modal_row[f"class_phi_{name}"] = float(class_phi[index])
+            modal_row[f"class_share_{name}"] = float(class_share[index])
+            modal_row[f"regression_phi_{name}"] = float(reg_phi[index])
+            modal_row[f"regression_share_{name}"] = float(reg_share[index])
+        interaction_row: dict[str, Any] = {"base_model": model_name, "case_id": case["case_id"]}
+        for key, label in (((0, 1), "TA"), ((0, 2), "TV"), ((1, 2), "AV")):
+            interaction_row[f"class_interaction_{label}"] = class_interactions[key]
+            interaction_row[f"regression_interaction_{label}"] = reg_interactions[key]
+        local, scale_maps = _local_occlusion(
+            model, xs, mask, float(logits[predicted]), intensity, predicted, device, explanation_batch_size
+        )
+        local_by_model[model_name] = local
+        scale_by_model[model_name] = scale_maps
+        router_row: dict[str, Any] | None = None
+        if model_name == "mofe":
+            router_row, router_map = _router_profile(model, xs, mask, device)
+            router_row["case_id"] = case["case_id"]
+            router_row["router_shapley_spearman"] = _spearman(
+                np.asarray([router_row[f"router_{name}_share"] for name in MODALITIES]), class_share
+            )
+            router_row["router_shapley_top1_agreement"] = bool(
+                np.argmax([router_row[f"router_{name}_share"] for name in MODALITIES]) == np.argmax(class_share)
+            )
+            router_by_model[model_name] = router_row
+            result.setdefault("router_local", {})[model_name] = router_map
+        result["models"][model_name] = {
+            "logits": logits,
+            "probabilities": probabilities,
+            "predicted_class": predicted,
+            "intensity": intensity,
+            "modal_row": modal_row,
+            "interaction_row": interaction_row,
+            "router_row": router_row,
+            "local": local,
+            "scale_maps": scale_maps,
+        }
+        result["variant_rows"].append(
+            {
+                "variant": "E0_EarlyConcat" if model_name == "early_concat" else "E1_MoFE_Router",
+                "base_model": model_name,
+                "case_id": case["case_id"],
+                "predicted_class": predicted,
+                "predicted_class_name": CLASS_NAMES[predicted],
+                "predicted_sentiment": intensity,
+                "confidence": float(probabilities[predicted]),
+                "p_negative": float(probabilities[0]),
+                "p_neutral": float(probabilities[1]),
+                "p_positive": float(probabilities[2]),
+                "source_video": case["video_path"],
+                "explanation_method": "exact Shapley + multiscale occlusion" if model_name == "early_concat" else "router profile; not itself a counterfactual contribution",
+            }
+        )
+        if model_name == "mofe":
+            result["variant_rows"].append(
+                {
+                    "variant": "E2_MoFE_Shapley",
+                    "base_model": model_name,
+                    "case_id": case["case_id"],
+                    "predicted_class": predicted,
+                    "predicted_class_name": CLASS_NAMES[predicted],
+                    "predicted_sentiment": intensity,
+                    "confidence": float(probabilities[predicted]),
+                    "p_negative": float(probabilities[0]),
+                    "p_neutral": float(probabilities[1]),
+                    "p_positive": float(probabilities[2]),
+                    "source_video": case["video_path"],
+                    "explanation_method": "exact Shapley + multiscale occlusion",
+                }
+            )
+    result["local_by_model"] = local_by_model
+    result["scale_by_model"] = scale_by_model
+    result["router_by_model"] = router_by_model
+    return result
+
+
+def run(args: argparse.Namespace) -> None:
+    out_dir = args.output_dir.expanduser().resolve()
+    out_dir.mkdir(parents=True, exist_ok=True)
+    remaining = [p for p in out_dir.iterdir() if p.name != ".gitkeep"]
+    if remaining and not args.resume:
+        raise FileExistsError(f"output directory is not empty; choose a new path: {out_dir}")
+    if remaining:
+        print(f"Reusing existing Q3 output directory after a partial run: {out_dir}", flush=True)
+    started = time.time()
+    for path in (args.early_checkpoint, args.mofe_checkpoint, args.scaler):
+        if not path.is_file():
+            raise FileNotFoundError(f"Q2 model artifact not found: {path}")
+    centers, scales = _load_scaler(args.scaler)
+    cases, input_locations = _read_attachment4(args.attachment4_version)
+    dims = tuple(int(x.shape[-1]) for x in cases[0]["features"])
+    device = torch.device("cuda" if args.device == "auto" and torch.cuda.is_available() else ("cpu" if args.device == "auto" else args.device))
+    if device.type == "cuda":
+        torch.cuda.manual_seed_all(SEED)
+        torch.set_float32_matmul_precision("high")
+    torch.set_num_threads(4)
+    models = {
+        "early_concat": _build_model("early_concat", dims, args.early_checkpoint, device),
+        "mofe": _build_model("mofe", dims, args.mofe_checkpoint, device),
+    }
+    for case in cases:
+        case["features"] = _scale_features(case["features"], case["mask"], centers, scales)
+    tokenizer = None
+    if AutoTokenizer is not None:
+        try:
+            tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased", use_fast=True, local_files_only=True)
+        except Exception:
+            tokenizer = None
+
+    prediction_rows: list[dict[str, Any]] = []
+    shapley_rows: list[dict[str, Any]] = []
+    interaction_rows: list[dict[str, Any]] = []
+    local_rows: list[dict[str, Any]] = []
+    router_rows: list[dict[str, Any]] = []
+    router_local_rows: list[dict[str, Any]] = []
+    faithfulness_rows: list[dict[str, Any]] = []
+    segment_rows: list[dict[str, Any]] = []
+    profiles_for_summary: dict[str, list[np.ndarray]] = {variant[0]: [] for variant in MODEL_VARIANTS}
+    result_for_cards: list[dict[str, Any]] = []
+    shapley_by_key: dict[tuple[str, str], dict[str, Any]] = {}
+    interaction_by_key: dict[tuple[str, str], dict[str, Any]] = {}
+    router_by_id: dict[str, dict[str, Any]] = {}
+    validation_models: dict[str, tuple[torch.nn.Module, np.ndarray, np.ndarray]] = {}
+
+    for index, case in enumerate(cases, start=1):
+        print(f"Q3 Attachment 4: explaining {case['case_id']} ({index}/{len(cases)})", flush=True)
+        evaluated = _evaluate_case(
+            case, models, device, args.explanation_batch_size, tokenizer, out_dir, not args.no_frames
+        )
+        for model_name in ("early_concat", "mofe"):
+            model_result = evaluated["models"][model_name]
+            shap_row = model_result["modal_row"]
+            interaction_row = model_result["interaction_row"]
+            shapley_rows.append(shap_row)
+            interaction_rows.append(interaction_row)
+            shapley_by_key[(model_name, case["case_id"])] = shap_row
+            interaction_by_key[(model_name, case["case_id"])] = interaction_row
+            if model_name == "mofe":
+                router_by_id[case["case_id"]] = model_result["router_row"]
+                router_rows.append(model_result["router_row"])
+            for (modality, slot), scores in model_result["local"].items():
+                provenance_rows = _source_rows(case, modality, [slot])
+                interval = case["target_intervals"][slot]
+                duration = case["video_duration_sec"]
+                local_rows.append(
+                    {
+                        "base_model": model_name,
+                        "case_id": case["case_id"],
+                        "modality": MODALITIES[modality],
+                        "slot_0based": slot,
+                        "relative_progress_start": float(interval[0]),
+                        "relative_progress_end": float(interval[1]),
+                        **scores,
+                        "source_row_start": min(provenance_rows) if provenance_rows else 0,
+                        "source_row_end_exclusive": max(provenance_rows) + 1 if provenance_rows else 0,
+                        "source_video": case["video_path"],
+                        "video_time_start_sec_estimate": float(interval[0] * duration) if duration else None,
+                        "video_time_end_sec_estimate": float(interval[1] * duration) if duration else None,
+                    }
+                )
+            stability = _scale_stability(model_result["scale_maps"], case["mask"])
+            local_map = np.full((3, 50), np.nan, dtype=np.float32)
+            for (modality, slot), scores in model_result["local"].items():
+                local_map[modality, slot] = scores["class_logit_drop_multiscale"]
+            if model_name == "early_concat":
+                variant = "E0_EarlyConcat"
+                importance = np.nan_to_num(local_map, nan=0.0)
+                router_display = False
+            else:
+                variant = "E2_MoFE_Shapley"
+                importance = np.nan_to_num(local_map, nan=0.0)
+                router_display = False
+            faith = _faithfulness(
+                models[model_name], case["features"], case["mask"],
+                float(model_result["logits"][model_result["predicted_class"]]),
+                int(model_result["predicted_class"]), np.abs(importance), device,
+            )
+            faith.update(
+                {
+                    "variant": variant,
+                    "case_id": case["case_id"],
+                    "scale_stability_mean_spearman": stability["mean_scale_spearman"],
+                    "scale_stability_w1_w3": stability["spearman_w1_w3"],
+                    "scale_stability_w1_w5": stability["spearman_w1_w5"],
+                    "scale_stability_w3_w5": stability["spearman_w3_w5"],
+                }
+            )
+            faithfulness_rows.append(faith)
+            segment_rows.extend(
+                _segment_profile(importance, case["mask"], case, variant, tokenizer, out_dir, not args.no_frames)
+            )
+            profiles_for_summary[variant].append(importance)
+            if model_name == "mofe":
+                router_map = evaluated["router_local"]["mofe"]
+                router_profile = np.nan_to_num(router_map, nan=0.0)
+                router_faith = _faithfulness(
+                    models["mofe"], case["features"], case["mask"],
+                    float(model_result["logits"][model_result["predicted_class"]]),
+                    int(model_result["predicted_class"]), router_profile, device,
+                )
+                router_faith.update({"variant": "E1_MoFE_Router", "case_id": case["case_id"]})
+                faithfulness_rows.append(router_faith)
+                segment_rows.extend(
+                    _segment_profile(router_profile, case["mask"], case, "E1_MoFE_Router", tokenizer, out_dir, not args.no_frames)
+                )
+                profiles_for_summary["E1_MoFE_Router"].append(router_profile)
+                _plot_profile(
+                    out_dir / "evidence_profiles" / "E1_MoFE_Router" / f"{_safe_name(case['case_id'])}.png",
+                    router_profile, case["mask"], f"E1 MoFE router utility — {case['case_id']}", router=True,
+                )
+                for modality in range(3):
+                    for slot in range(50):
+                        if case["mask"][slot, modality]:
+                            router_local_rows.append(
+                                {
+                                    "case_id": case["case_id"],
+                                    "modality": MODALITIES[modality],
+                                    "slot_0based": slot,
+                                    "router_utility": float(router_map[modality, slot]),
+                                    "relative_progress_start": float(case["target_intervals"][slot, 0]),
+                                    "relative_progress_end": float(case["target_intervals"][slot, 1]),
+                                }
+                            )
+            display_profile = router_profile if model_name == "mofe" and router_display else importance
+            _plot_profile(
+                out_dir / "evidence_profiles" / variant / f"{_safe_name(case['case_id'])}.png",
+                display_profile, case["mask"], f"{variant} — {case['case_id']}", router=router_display,
+            )
+
+        result_for_cards.extend(evaluated["variant_rows"])
+        for row in evaluated["variant_rows"]:
+            prediction_rows.append(row)
+        validation_models["early_concat"] = (
+            models["early_concat"],
+            evaluated["models"]["early_concat"]["logits"],
+            np.asarray([evaluated["models"]["early_concat"]["intensity"]]),
+        )
+        validation_models["mofe"] = (
+            models["mofe"],
+            evaluated["models"]["mofe"]["logits"],
+            np.asarray([evaluated["models"]["mofe"]["intensity"]]),
+        )
+
+    _write_csv(out_dir / "attachment4_predictions.csv", prediction_rows)
+    _write_csv(out_dir / "attachment4_modal_shapley.csv", shapley_rows)
+    _write_csv(out_dir / "attachment4_pairwise_interactions.csv", interaction_rows)
+    _write_csv(out_dir / "attachment4_local_evidence.csv", local_rows)
+    _write_csv(out_dir / "attachment4_router_profiles.csv", router_rows)
+    _write_csv(out_dir / "attachment4_router_local_evidence.csv", router_local_rows)
+    _write_csv(out_dir / "attachment4_evidence_segments.csv", segment_rows)
+    _write_csv(out_dir / "faithfulness_by_sample.csv", faithfulness_rows)
+    typical_id = _make_cards(
+        out_dir, cases, result_for_cards, shapley_by_key, interaction_by_key, router_by_id, segment_rows, faithfulness_rows
+    )
+    if not args.skip_validation:
+        validation_path = args.validation_data.expanduser().resolve()
+        valid = _load_validation(validation_path, centers, scales)
+        validation_metrics, validation_error_count = _validation_errors(
+            valid[0], valid[1], valid[2], valid[3], valid[4],
+            validation_models, device, args.validation_batch_size, out_dir,
+        )
+        del valid
+    else:
+        validation_metrics, validation_error_count = {}, 0
+
+    reference = _q2_reference_metrics(args.q2_validation_reference.expanduser().resolve())
+    summary_rows: list[dict[str, Any]] = []
+    for variant, model_name, method in MODEL_VARIANTS:
+        variant_faith = [row for row in faithfulness_rows if row["variant"] == variant]
+        mean = lambda key: float(np.mean([float(row[key]) for row in variant_faith if row.get(key) is not None])) if any(row.get(key) is not None for row in variant_faith) else None
+        val_name = "EarlyConcat" if model_name == "early_concat" else "MoFE-7"
+        q2_metrics = reference.get(val_name, {})
+        q3_val = validation_metrics.get(model_name, {})
+        summary_rows.append(
+            {
+                "variant": variant,
+                "backbone": "EarlyConcat + BiGRU" if model_name == "early_concat" else "MoFE-7 + MLP Router",
+                "explanation_method": method,
+                "validation_accuracy": q3_val.get("accuracy", q2_metrics.get("accuracy")),
+                "validation_macro_f1": q3_val.get("macro_f1", q2_metrics.get("macro_f1")),
+                "validation_mae": q3_val.get("mae", q2_metrics.get("mae")),
+                "validation_rmse": q3_val.get("rmse", q2_metrics.get("rmse")),
+                "validation_pearson": q3_val.get("pearson", q2_metrics.get("pearson")),
+                "attachment4_cases": len(cases),
+                "comprehensiveness_delete_10pct_mean": mean("comprehensiveness_delete_10pct"),
+                "comprehensiveness_delete_30pct_mean": mean("comprehensiveness_delete_30pct"),
+                "sufficiency_abs_gap_retain_10pct_mean": mean("sufficiency_abs_gap_retain_10pct"),
+                "sufficiency_abs_gap_retain_30pct_mean": mean("sufficiency_abs_gap_retain_30pct"),
+                "deletion_auc_0_70_mean_logit_drop": mean("deletion_auc_0_70_mean_logit_drop"),
+                "scale_stability_mean_spearman": mean("scale_stability_mean_spearman"),
+                "router_shapley_mean_spearman": (
+                    float(np.mean([row["router_shapley_spearman"] for row in router_rows if row.get("router_shapley_spearman") is not None]))
+                    if model_name == "mofe" and any(row.get("router_shapley_spearman") is not None for row in router_rows)
+                    else None
+                ),
+                "router_shapley_top1_agreement_rate": (
+                    float(np.mean([bool(row["router_shapley_top1_agreement"]) for row in router_rows]))
+                    if model_name == "mofe" else None
+                ),
+            }
+        )
+    _write_csv(out_dir / "q3_method_comparison.csv", summary_rows)
+
+    completeness = [
+        abs(float(row[key]))
+        for row in shapley_rows
+        for key in ("class_completeness_residual", "regression_completeness_residual")
+    ]
+    if completeness and max(completeness) > 1e-4:
+        raise AssertionError(f"exact Shapley completeness check failed: max residual {max(completeness)}")
+    manifest = {
+        "experiment": "Q3 first-round hierarchical counterfactual evidence attribution",
+        "created_at_unix": time.time(),
+        "elapsed_seconds": time.time() - started,
+        "seed": SEED,
+        "device": str(device),
+        "attachment4": input_locations,
+        "attachment4_version": args.attachment4_version,
+        "attachment4_cases": len(cases),
+        "coordinate_mode": "relative normalized progress",
+        "physical_time_alignment": False,
+        "time_mapping_limit": "estimated clip seconds equal normalized progress times video duration; original unaligned rows have no physical timestamps",
+        "models": {
+            "E0_EarlyConcat": {"checkpoint": str(args.early_checkpoint), "sha256": _sha256(args.early_checkpoint)},
+            "E1_E2_MoFE": {"checkpoint": str(args.mofe_checkpoint), "sha256": _sha256(args.mofe_checkpoint)},
+        },
+        "scaler": {"path": str(args.scaler), "sha256": _sha256(args.scaler), "fit": "Q2 official training rows only"},
+        "shapley": {
+            "coalitions": [sorted(x) for x in COALITIONS],
+            "class_value": "full-input predicted-class logit, fixed class across coalitions",
+            "regression_value": "predicted intensity",
+            "exact_enumeration": True,
+            "max_completeness_residual": max(completeness) if completeness else None,
+        },
+        "interaction": "pairwise Shapley interaction index; positive values indicate synergistic logit/intensity interaction under this convention",
+        "local_evidence": {"method": "leave out a 1, 3, or 5-bin contiguous window from one modality", "windows": list(WINDOWS), "scale_weights": [1 / 3] * 3},
+        "router_note": "MoFE router exposure is an internal routing summary, not prediction contribution; compare its rank with exact Shapley and deletion faithfulness.",
+        "faithfulness": {
+            "score": "fixed predicted-class logit",
+            "comprehensiveness": "full score minus score after deleting top-ranked cells",
+            "sufficiency": "full score minus score with only top-ranked cells retained",
+            "deletion_auc_fraction_range": [0.0, 0.7],
+            "mask_training_rates": [0.0, 0.1, 0.3, 0.5, 0.7],
+            "limit": "isolated sparse masks may still differ from the contiguous masks used in training; 90% deletion/10% retention is not claimed as in-distribution",
+        },
+        "validation_metrics": validation_metrics,
+        "validation_error_examples": validation_error_count,
+        "q2_validation_reference": reference,
+        "tokenizer_available": tokenizer is not None,
+        "outputs": [
+            "attachment4_predictions.csv", "attachment4_modal_shapley.csv",
+            "attachment4_pairwise_interactions.csv", "attachment4_local_evidence.csv",
+            "attachment4_router_profiles.csv", "attachment4_router_local_evidence.csv",
+            "attachment4_evidence_segments.csv", "faithfulness_by_sample.csv",
+            "q3_method_comparison.csv", "explanation_cards/", "evidence_profiles/",
+            "evidence_frames/", "typical_explanation_card.md",
+        ] + ([] if args.skip_validation else ["validation_metrics.json", "validation_predictions.csv", "validation_errors.csv", "validation_error_attribution.csv"]),
+        "typical_explanation_case": typical_id,
+    }
+    _write_json(out_dir / "run_manifest.json", manifest)
+    print(f"Q3 complete: {len(cases)} Attachment 4 cases; outputs saved under {out_dir}", flush=True)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--attachment4-version", choices=("unaligned_50",), default="unaligned_50")
+    parser.add_argument("--early-checkpoint", type=Path, default=DEFAULT_EARLY)
+    parser.add_argument("--mofe-checkpoint", type=Path, default=DEFAULT_MOFE)
+    parser.add_argument("--scaler", type=Path, default=DEFAULT_SCALER)
+    parser.add_argument("--validation-data", type=Path, default=ATTACHMENT2 / "unaligned_50.pkl")
+    parser.add_argument("--q2-validation-reference", type=Path, default=PROJECT_ROOT / "output" / "q2" / "comparison_validation.csv")
+    parser.add_argument("--output-dir", type=Path, default=PROJECT_ROOT / "output" / "q3")
+    parser.add_argument("--device", choices=("auto", "cpu", "cuda"), default="auto")
+    parser.add_argument("--explanation-batch-size", type=int, default=128)
+    parser.add_argument("--validation-batch-size", type=int, default=128)
+    parser.add_argument("--skip-validation", action="store_true", help="Skip the labeled official validation split.")
+    parser.add_argument("--no-frames", action="store_true", help="Do not extract approximate candidate frames from source videos.")
+    parser.add_argument("--resume", action="store_true", help="Rerun into an existing partial output directory, overwriting this run's outputs.")
+    args = parser.parse_args()
+    run(args)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/final/q3/test_explanations.py b/final/q3/test_explanations.py
new file mode 100644
index 0000000..a037004
--- /dev/null
+++ b/final/q3/test_explanations.py
@@ -0,0 +1,52 @@
+from __future__ import annotations
+
+import itertools
+import unittest
+
+import numpy as np
+
+from .run_experiments import COALITIONS, exact_pair_interactions, exact_shapley, _scale_features
+
+
+class ExactAttributionTests(unittest.TestCase):
+    def test_additive_game_shapley_and_interaction(self) -> None:
+        weights = (1.25, -0.5, 2.0)
+        values = {
+            coalition: 3.0 + sum(weights[player] for player in coalition)
+            for coalition in COALITIONS
+        }
+        np.testing.assert_allclose(exact_shapley(values), weights, atol=1e-12)
+        for interaction in exact_pair_interactions(values).values():
+            self.assertAlmostEqual(interaction, 0.0, places=12)
+
+    def test_pair_interaction_is_reported_with_standard_half_weight(self) -> None:
+        values = {}
+        for coalition in COALITIONS:
+            value = float(len(coalition))
+            if 0 in coalition and 1 in coalition:
+                value += 2.0
+            values[coalition] = value
+        interactions = exact_pair_interactions(values)
+        self.assertAlmostEqual(interactions[(0, 1)], 1.0, places=12)
+        self.assertAlmostEqual(interactions[(0, 2)], 0.0, places=12)
+        self.assertAlmostEqual(interactions[(1, 2)], 0.0, places=12)
+
+    def test_robust_scaling_supports_single_cases_and_validation_batches(self) -> None:
+        features = (np.full((2, 4, 2), 3.0, np.float32),)
+        centers = (np.asarray([1.0, 1.0], np.float32),)
+        scales = (np.asarray([2.0, 2.0], np.float32),)
+        single_mask = np.ones((4, 1), dtype=bool)
+        single = _scale_features((features[0][0],), single_mask, centers, scales)[0]
+        self.assertEqual(single.shape, (4, 2))
+        np.testing.assert_allclose(single, 1.0)
+        batch_mask = np.ones((2, 4, 1), dtype=bool)
+        batch_mask[1, 2:, 0] = False
+        batch = _scale_features(features, batch_mask, centers, scales)[0]
+        self.assertEqual(batch.shape, (2, 4, 2))
+        np.testing.assert_allclose(batch[0], 1.0)
+        np.testing.assert_allclose(batch[1, :2], 1.0)
+        np.testing.assert_allclose(batch[1, 2:], 0.0)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/final/q3/train_interpretable.py b/final/q3/train_interpretable.py
index ef92bc1..95ad31b 100644
--- a/final/q3/train_interpretable.py
+++ b/final/q3/train_interpretable.py
@@ -1,569 +1,6 @@
-"""Train Q3 on the official training split and explain Attachment 4 cases."""
-from __future__ import annotations
+"""Compatibility entry point for the first Q3 explanation experiment."""
 
-import argparse
-import csv
-import hashlib
-import json
-import math
-import random
-import time
-from pathlib import Path
-from typing import Any
-
-import matplotlib
-
-matplotlib.use("Agg")
-import matplotlib.pyplot as plt
-import numpy as np
-import torch
-import torch.nn.functional as F
-from sklearn.metrics import accuracy_score, confusion_matrix, f1_score, mean_absolute_error, mean_squared_error
-from transformers import AutoTokenizer
-
-from ..adapter import Q1AlignmentAdapter
-from ..data_paths import ATTACHMENT4, DATA_ROOT, PROJECT_ROOT
-from ..model.early_concat import AlignedFusionModel
-from ..q2.deep_learning.q2.evaluate_math_protocol import continuous_mask, scenario_seed
-from ..q2.math.data import (
-    MODALITIES,
-    fit_preprocessor,
-    load_official_splits,
-    restricted_load,
-    transform_split,
-)
-
-SEED = 20260924
-TEXT_MODEL_ID = "google-bert/bert-base-uncased"
-CLASS_NAMES = ("negative", "neutral", "positive")
-MODALITY_NAMES = ("text", "audio", "vision")
-
-
-def _sha256(path: Path) -> str:
-    h = hashlib.sha256()
-    with path.open("rb") as stream:
-        for block in iter(lambda: stream.read(1024 * 1024), b""):
-            h.update(block)
-    return h.hexdigest()
-
-
-def _decode(value: Any) -> str:
-    if isinstance(value, bytes):
-        return value.decode("utf-8", errors="replace")
-    if isinstance(value, np.bytes_):
-        return bytes(value).decode("utf-8", errors="replace")
-    if isinstance(value, np.ndarray):
-        if value.shape == ():
-            return _decode(value.item())
-        return " ".join(_decode(x) for x in value.reshape(-1))
-    return str(value)
-
-
-def _scalar_int(value: Any, field: str) -> int:
-    arr = np.asarray(value).reshape(-1)
-    if not len(arr):
-        raise ValueError(f"Attachment 4 {field} is empty")
-    return int(arr[0])
-
-
-def _attachment4_location(version: str) -> tuple[Path, Path]:
-    inner = ATTACHMENT4 / "附件4-可解释专项视频样本与特征文件"
-    version_dir = inner / ("未对齐版本" if version == "unaligned_50" else "对齐版本")
-    video_dir = inner / "videos"
-    if not version_dir.is_dir():
-        raise FileNotFoundError(f"Attachment 4 {version} directory not found: {version_dir}")
-    return version_dir, video_dir
-
-
-def _read_attachment4(version: str) -> tuple[list[dict[str, Any]], dict[str, str]]:
-    if version != "unaligned_50":
-        raise ValueError("Q3 explanation currently uses the official unaligned_50 Attachment 4 features")
-    version_dir, video_dir = _attachment4_location(version)
-    paths = sorted(version_dir.glob("*.pkl"), key=lambda p: p.name)
-    if len(paths) != 20:
-        raise FileNotFoundError(f"expected 20 Attachment 4 cases, found {len(paths)} under {version_dir}")
-    video_by_stem = {p.stem: p for p in video_dir.rglob("*.mp4")} if video_dir.is_dir() else {}
-    adapter = Q1AlignmentAdapter(target_steps=50)
-    cases: list[dict[str, Any]] = []
-    for path in paths:
-        raw = restricted_load(path)
-        case_id = _decode(raw.get("id", path.stem)).strip() or path.stem
-        text_bert = np.asarray(raw["text_bert"], dtype=np.int64)
-        if text_bert.ndim == 3 and text_bert.shape[0] == 1:
-            text_bert = text_bert[0]
-        if text_bert.shape != (3, 50):
-            raise ValueError(f"{path.name}: expected text_bert (3,50), got {text_bert.shape}")
-        record = {
-            "id": case_id,
-            "sequence_order_verified": True,
-            "attention_mask": text_bert[1].astype(bool),
-            "text": np.asarray(raw["text"], dtype=np.float32),
-            "audio": np.asarray(raw["audio"], dtype=np.float32),
-            "vision": np.asarray(raw["vision"], dtype=np.float32),
-            "audio_length": _scalar_int(raw["audio_lengths"], "audio_lengths"),
-            "vision_length": _scalar_int(raw["vision_lengths"], "vision_lengths"),
-        }
-        aligned = adapter.align(record, mode="relative")
-        mask = np.stack([aligned.observed[m] for m in MODALITIES], axis=-1)
-        features = {m: aligned.features[m].astype(np.float32) for m in MODALITIES}
-        transcript = _decode(raw.get("raw_text", ""))
-        video_path = video_by_stem.get(path.stem) or video_by_stem.get(case_id)
-        media = ""
-        if video_path is not None:
-            try:
-                media = video_path.resolve().relative_to(DATA_ROOT).as_posix()
-            except ValueError:
-                media = str(video_path.resolve())
-        cases.append({
-            "case_id": case_id,
-            "source_file": path,
-            "source_sha256": _sha256(path),
-            "transcript": transcript,
-            "text_bert": text_bert,
-            "raw": raw,
-            "features": features,
-            "mask": mask,
-            "target_intervals": aligned.target_intervals.astype(np.float32),
-            "provenance": aligned.provenance,
-            "video_path": media,
-            "coordinate_mode": aligned.metadata["coordinate_mode"],
-            "input_audit": {
-                "case_id": case_id,
-                "source_file": path.name,
-                "source_sha256": _sha256(path),
-                "coordinate_mode": aligned.metadata["coordinate_mode"],
-                "physical_time_alignment": False,
-                "audio_reported_length": record["audio_length"],
-                "vision_reported_length": record["vision_length"],
-                "audio_length_conflict": bool(aligned.provenance["audio"].length_conflict),
-                "vision_length_conflict": bool(aligned.provenance["vision"].length_conflict),
-                "text_visible_target_slots": int(aligned.observed["text"].sum()),
-                "audio_visible_target_slots": int(aligned.observed["audio"].sum()),
-                "vision_visible_target_slots": int(aligned.observed["vision"].sum()),
-                "source_video": media,
-            },
-        })
-    return cases, {"version_dir": str(version_dir), "video_dir": str(video_dir)}
-
-
-def _split_arrays(split: Any, transformed: dict[str, np.ndarray]) -> tuple[tuple[np.ndarray, ...], np.ndarray]:
-    return tuple(transformed[m] for m in MODALITIES), np.asarray(split.mask, dtype=bool)
-
-
-def _predict(
-    model: torch.nn.Module,
-    xs: tuple[np.ndarray, ...],
-    masks: np.ndarray,
-    device: torch.device,
-    batch_size: int,
-) -> dict[str, np.ndarray]:
-    model.eval()
-    logits: list[np.ndarray] = []
-    intensity: list[np.ndarray] = []
-    with torch.inference_mode():
-        for start in range(0, len(masks), batch_size):
-            end = min(start + batch_size, len(masks))
-            batch_x = tuple(torch.as_tensor(x[start:end], dtype=torch.float32, device=device) for x in xs)
-            batch_mask = torch.as_tensor(masks[start:end], dtype=torch.bool, device=device)
-            output = model(batch_x, batch_mask)
-            logits.append(output["logits"].float().cpu().numpy())
-            intensity.append(output["intensity"].float().cpu().numpy())
-    return {"logits": np.concatenate(logits), "intensity": np.concatenate(intensity)}
-
-
-def _metrics(y_cls: np.ndarray, y_reg: np.ndarray, prediction: dict[str, np.ndarray]) -> dict[str, Any]:
-    logits = np.asarray(prediction["logits"])
-    score = np.clip(np.asarray(prediction["intensity"]).reshape(-1), -3.0, 3.0)
-    predicted = logits.argmax(axis=-1)
-    pearson = float(np.corrcoef(y_reg, score)[0, 1]) if np.std(y_reg) > 0 and np.std(score) > 0 else None
-    return {
-        "n": int(len(y_cls)),
-        "accuracy": float(accuracy_score(y_cls, predicted)),
-        "macro_f1": float(f1_score(y_cls, predicted, labels=[0, 1, 2], average="macro", zero_division=0)),
-        "mae": float(mean_absolute_error(y_reg, score)),
-        "rmse": float(math.sqrt(mean_squared_error(y_reg, score))),
-        "pearson": pearson,
-        "confusion_matrix_rows_true_columns_predicted": confusion_matrix(y_cls, predicted, labels=[0, 1, 2]).tolist(),
-        "per_class_support": {CLASS_NAMES[i]: int(np.sum(y_cls == i)) for i in range(3)},
-    }
-
-
-def _loss(logits: torch.Tensor, intensity: torch.Tensor, y_cls: torch.Tensor, y_reg: torch.Tensor) -> torch.Tensor:
-    return F.cross_entropy(logits, y_cls) + 0.5 * F.smooth_l1_loss(intensity / 3.0, y_reg / 3.0)
-
-
-def _validation_loss(
-    model: torch.nn.Module,
-    xs: tuple[np.ndarray, ...],
-    masks: list[np.ndarray],
-    y_cls: np.ndarray,
-    y_reg: np.ndarray,
-    device: torch.device,
-    batch_size: int,
-) -> float:
-    values: list[float] = []
-    model.eval()
-    with torch.inference_mode():
-        for scenario in masks:
-            total, count = 0.0, 0
-            for start in range(0, len(y_cls), batch_size):
-                end = min(start + batch_size, len(y_cls))
-                bx = tuple(torch.as_tensor(x[start:end], dtype=torch.float32, device=device) for x in xs)
-                bm = torch.as_tensor(scenario[start:end], dtype=torch.bool, device=device)
-                by = torch.as_tensor(y_cls[start:end], dtype=torch.long, device=device)
-                br = torch.as_tensor(y_reg[start:end], dtype=torch.float32, device=device)
-                out = model(bx, bm)
-                total += float(_loss(out["logits"], out["intensity"], by, br).item()) * (end - start)
-                count += end - start
-            values.append(total / max(1, count))
-    return float(np.mean(values))
-
-
-def _train(args: argparse.Namespace, out_dir: Path) -> tuple[AlignedFusionModel, dict[str, Any], dict[str, Any]]:
-    feature_path: Path
-    if args.data_path is not None:
-        feature_path = args.data_path.expanduser().resolve()
-    else:
-        from ..data_paths import ATTACHMENT2
-        feature_path = ATTACHMENT2 / f"{args.input_version}.pkl"
-    if not feature_path.is_file():
-        raise FileNotFoundError(f"Q3 training feature file not found: {feature_path}")
-    raw_splits = load_official_splits(feature_path, version=args.input_version)
-    train = raw_splits["train"]
-    valid = raw_splits["valid"]
-    fitted = fit_preprocessor(train)
-    transformed = {name: transform_split(split, fitted) for name, split in raw_splits.items()}
-    train_x, train_mask = _split_arrays(train, transformed["train"])
-    valid_x, valid_mask = _split_arrays(valid, transformed["valid"])
-    dims = tuple(int(x.shape[-1]) for x in train_x)
-
-    np.savez_compressed(out_dir / "preprocessor.npz", **{
-        f"{modality}_{key}": value for modality, state in fitted.items() for key, value in state.items()
-    })
-    device = torch.device("cuda" if args.device == "auto" and torch.cuda.is_available() else ("cpu" if args.device == "auto" else args.device))
-    if device.type == "cuda":
-        torch.cuda.manual_seed_all(SEED)
-    random.seed(SEED)
-    np.random.seed(SEED)
-    torch.manual_seed(SEED)
-    torch.set_num_threads(4)
-    model = AlignedFusionModel("concat", dims=dims).to(device)
-    optimizer = torch.optim.AdamW(model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay)
-    y_cls = np.asarray(train.class_y, dtype=np.int64)
-    y_reg = np.asarray(train.regression_y, dtype=np.float32)
-    vy_cls = np.asarray(valid.class_y, dtype=np.int64)
-    vy_reg = np.asarray(valid.regression_y, dtype=np.float32)
-    valid_rng_masks: list[np.ndarray] = [valid_mask.copy()]
-    for rate, mode in ((0.3, "single"), (0.3, "sync"), (0.5, "async")):
-        key = f"{rate:.1f}/{mode}"
-        valid_rng_masks.append(np.stack([
-            continuous_mask(mask, rate, mode, np.random.default_rng(scenario_seed(SEED + 177, sid, key)))
-            for sid, mask in zip(valid.ids, valid_mask)
-        ]))
-
-    best = float("inf")
-    best_epoch = 0
-    stale = 0
-    history: list[dict[str, Any]] = []
-    for epoch in range(1, args.epochs + 1):
-        model.train()
-        train_corruption = np.stack([
-            continuous_mask(
-                mask,
-                float(np.random.choice((0.0, 0.1, 0.3, 0.5, 0.7))),
-                str(np.random.choice(("single", "sync", "partial", "async"))),
-                np.random.default_rng(scenario_seed(SEED + epoch, sid, f"train/{epoch}")),
-            )
-            for sid, mask in zip(train.ids, train_mask)
-        ])
-        order = np.random.permutation(len(y_cls))
-        losses: list[float] = []
-        for start in range(0, len(order), args.batch_size):
-            ix = order[start:start + args.batch_size]
-            bx = tuple(torch.as_tensor(x[ix], dtype=torch.float32, device=device) for x in train_x)
-            bm = torch.as_tensor(train_corruption[ix], dtype=torch.bool, device=device)
-            by = torch.as_tensor(y_cls[ix], dtype=torch.long, device=device)
-            br = torch.as_tensor(y_reg[ix], dtype=torch.float32, device=device)
-            optimizer.zero_grad(set_to_none=True)
-            output = model(bx, bm)
-            loss = _loss(output["logits"], output["intensity"], by, br)
-            loss.backward()
-            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
-            optimizer.step()
-            losses.append(float(loss.item()))
-        validation = _validation_loss(model, valid_x, valid_rng_masks, vy_cls, vy_reg, device, args.batch_size)
-        history.append({"epoch": epoch, "train_loss": float(np.mean(losses)), "selection_loss": validation})
-        print(f"Q3 epoch {epoch}/{args.epochs}: train={np.mean(losses):.5f}, validation={validation:.5f}", flush=True)
-        if validation < best - 1e-7:
-            best, best_epoch, stale = validation, epoch, 0
-            torch.save({"state_dict": model.state_dict(), "dims": dims, "seed": SEED, "best_epoch": epoch}, out_dir / "model_best.pt")
-        else:
-            stale += 1
-            if stale >= args.patience:
-                break
-    checkpoint = torch.load(out_dir / "model_best.pt", map_location=device, weights_only=True)
-    model.load_state_dict(checkpoint["state_dict"])
-    model.eval()
-    prediction = _predict(model, valid_x, valid_mask, device, args.batch_size)
-    metric = _metrics(vy_cls, vy_reg, prediction)
-    metric["best_epoch"] = best_epoch
-    metric["selection_loss_clean_plus_fixed_missing_scenarios"] = best
-    metric["input_version"] = args.input_version
-    metric["adapter"] = "Q1AlignmentAdapter relative normalized progress"
-    metric["physical_time_alignment"] = False
-    _write_csv(out_dir / "training_history.csv", history)
-    _write_json(out_dir / "validation_metrics.json", metric)
-    validation_rows = []
-    for i, sid in enumerate(valid.ids):
-        prob = torch.softmax(torch.as_tensor(prediction["logits"][i]), dim=-1).numpy()
-        validation_rows.append({
-            "sample_id": sid,
-            "true_class": int(vy_cls[i]),
-            "true_class_name": CLASS_NAMES[int(vy_cls[i])],
-            "true_sentiment": float(vy_reg[i]),
-            "predicted_class": int(prob.argmax()),
-            "predicted_class_name": CLASS_NAMES[int(prob.argmax())],
-            "predicted_sentiment": float(prediction["intensity"][i]),
-            "p_negative": float(prob[0]), "p_neutral": float(prob[1]), "p_positive": float(prob[2]),
-            "absolute_error": float(abs(vy_reg[i] - prediction["intensity"][i])),
-        })
-    _write_csv(out_dir / "validation_predictions.csv", validation_rows)
-    errors = sorted(
-        (row for row in validation_rows if row["true_class"] != row["predicted_class"] or row["absolute_error"] >= metric["mae"]),
-        key=lambda row: (-row["absolute_error"], row["sample_id"]),
-    )
-    _write_csv(out_dir / "validation_errors.csv", errors[:100])
-    return model, {"metrics": metric, "feature_sha256": _sha256(feature_path), "feature_path": str(feature_path)}, {"x": valid_x, "mask": valid_mask, "y_cls": vy_cls, "y_reg": vy_reg, "prediction": prediction}
-
-
-def _write_csv(path: Path, rows: list[dict[str, Any]]) -> None:
-    if not rows:
-        return
-    path.parent.mkdir(parents=True, exist_ok=True)
-    fields = list(dict.fromkeys(key for row in rows for key in row))
-    with path.open("w", encoding="utf-8-sig", newline="") as stream:
-        writer = csv.DictWriter(stream, fieldnames=fields)
-        writer.writeheader()
-        writer.writerows(rows)
-
-
-def _write_json(path: Path, payload: Any) -> None:
-    path.write_text(json.dumps(payload, ensure_ascii=False, indent=2, allow_nan=False), encoding="utf-8")
-
-
-def _model_output(model: torch.nn.Module, xs: tuple[torch.Tensor, ...], mask: torch.Tensor) -> dict[str, torch.Tensor]:
-    model.eval()
-    with torch.inference_mode():
-        return model(xs, mask)
-
-
-def _span_evidence(case: dict[str, Any], modality_index: int, slot: int, tokenizer: Any) -> dict[str, Any]:
-    modality = MODALITY_NAMES[modality_index]
-    weights = case["provenance"][modality].source_weights.getrow(slot)
-    source_rows = weights.indices.tolist()
-    if source_rows:
-        low, high = min(source_rows), max(source_rows) + 1
-    else:
-        low = high = 0
-    start, end = case["target_intervals"][slot].astype(float).tolist()
-    text = ""
-    if modality == "text" and source_rows:
-        ids = np.asarray(case["text_bert"][0], dtype=np.int64)
-        token_ids = [int(ids[i]) for i in source_rows if i < len(ids) and int(ids[i]) not in tokenizer.all_special_ids]
-        text = " ".join(tokenizer.convert_ids_to_tokens(token_ids))
-    elif modality == "audio":
-        text = f"audio feature rows {low}–{high - 1}; inspect the same relative span in the linked source video/audio"
-    else:
-        text = f"video feature rows {low}–{high - 1}; inspect the same relative span in the linked source video"
-    return {
-        "modality": modality,
-        "slot": int(slot),
-        "relative_start": float(start),
-        "relative_end": float(end),
-        "source_row_start": int(low),
-        "source_row_end_exclusive": int(high),
-        "evidence": text,
-    }
-
-
-def _explain_case(
-    model: torch.nn.Module,
-    case: dict[str, Any],
-    stats: dict[str, dict[str, np.ndarray]],
-    tokenizer: Any,
-    device: torch.device,
-    batch_size: int,
-) -> tuple[dict[str, Any], list[dict[str, Any]]]:
-    values: dict[str, np.ndarray] = {}
-    for modality_index, modality in enumerate(MODALITIES):
-        arr = case["features"][modality].astype(np.float32)
-        arr = np.clip((arr - stats[modality]["mean"]) / stats[modality]["std"], -10.0, 10.0)
-        arr[~case["mask"][:, modality_index]] = 0.0
-        values[modality] = arr
-    xs = tuple(torch.as_tensor(values[m][None], dtype=torch.float32, device=device) for m in MODALITIES)
-    mask = torch.as_tensor(case["mask"][None], dtype=torch.bool, device=device)
-    full = _model_output(model, xs, mask)
-    probs = torch.softmax(full["logits"], dim=-1)[0].cpu().numpy()
-    pred = int(np.argmax(probs))
-    contributions: dict[str, float] = {}
-    local_rows: list[dict[str, Any]] = []
-    for m, modality in enumerate(MODALITY_NAMES):
-        ablated_mask = mask.clone()
-        ablated_mask[:, :, m] = False
-        ablated = _model_output(model, xs, ablated_mask)
-        ablated_p = torch.softmax(ablated["logits"], dim=-1)[0, pred].item()
-        contributions[modality] = float(probs[pred] - ablated_p)
-        observed_slots = np.flatnonzero(case["mask"][:, m])
-        if not len(observed_slots):
-            continue
-        impacts: list[tuple[int, float]] = []
-        for start in range(0, len(observed_slots), batch_size):
-            chosen = observed_slots[start:start + batch_size]
-            bx = tuple(x.repeat(len(chosen), 1, 1) for x in xs)
-            bm = mask.repeat(len(chosen), 1, 1)
-            row_idx = torch.arange(len(chosen), device=device)
-            slot_idx = torch.as_tensor(chosen, dtype=torch.long, device=device)
-            bm[row_idx, slot_idx, m] = False
-            output = _model_output(model, bx, bm)
-            hidden_p = torch.softmax(output["logits"], dim=-1)[:, pred].cpu().numpy()
-            impacts.extend((int(slot), float(probs[pred] - p)) for slot, p in zip(chosen, hidden_p))
-        for slot, impact in sorted(impacts, key=lambda row: (-row[1], row[0]))[:3]:
-            evidence = _span_evidence(case, m, slot, tokenizer)
-            evidence["probability_drop"] = impact
-            evidence["case_id"] = case["case_id"]
-            evidence["source_video"] = case["video_path"]
-            local_rows.append(evidence)
-    principal = max(contributions, key=contributions.get)
-    intensity = float(full["intensity"][0].cpu().item())
-    explanation = {
-        "case_id": case["case_id"],
-        "predicted_class": pred,
-        "predicted_class_name": CLASS_NAMES[pred],
-        "predicted_sentiment": intensity,
-        "p_negative": float(probs[0]), "p_neutral": float(probs[1]), "p_positive": float(probs[2]),
-        "principal_modality": principal,
-        "text_contribution": contributions["text"],
-        "audio_contribution": contributions["audio"],
-        "vision_contribution": contributions["vision"],
-        "transcript": case["transcript"],
-        "source_video": case["video_path"],
-        "coordinate_mode": case["coordinate_mode"],
-        "interpretation_method": "single-modality and single-slot occlusion; probability drops measure model sensitivity",
-    }
-    return explanation, local_rows
-
-
-def _write_cards(out_dir: Path, case_by_id: dict[str, dict[str, Any]], explanations: list[dict[str, Any]], local_rows: list[dict[str, Any]]) -> str:
-    cards = out_dir / "explanation_cards"
-    cards.mkdir(parents=True, exist_ok=True)
-    rows_by_id: dict[str, list[dict[str, Any]]] = {}
-    for row in local_rows:
-        rows_by_id.setdefault(str(row["case_id"]), []).append(row)
-    for item in explanations:
-        evidence = rows_by_id.get(str(item["case_id"]), [])
-        lines = [f"# Q3 Explanation: {item['case_id']}", "", f"- Prediction: **{item['predicted_class_name']}**", f"- Sentiment score: {item['predicted_sentiment']:.3f}", f"- Probabilities (negative / neutral / positive): {item['p_negative']:.3f} / {item['p_neutral']:.3f} / {item['p_positive']:.3f}", f"- Main modality by occlusion: **{item['principal_modality']}**", f"- Source video/audio: `{item['source_video'] or 'not found in the supplied video folder'}`", f"- Coordinate: normalized progress `[0,1]`; no physical timestamps are inferred from the unaligned feature rows.", "", "## Modality contribution", "", "Removing one modality changes the predicted-class probability by the values below. Positive values mean that modality supports the prediction under this model.", "", "| Modality | Probability drop |", "|---|---:|"]
-        for modality in MODALITY_NAMES:
-            lines.append(f"| {modality} | {item[f'{modality}_contribution']:.4f} |")
-        lines.extend(["", "## Local evidence", "", "Local values are single-slot occlusion sensitivity. Audio/video spans are relative positions in the supplied source clip; text is shown as BERT tokens and the full transcript is retained below.", ""])
-        for evidence_row in evidence:
-            lines.append(f"- **{evidence_row['modality']}**, slots {evidence_row['slot']} `[0-based]`, relative {evidence_row['relative_start']:.3f}–{evidence_row['relative_end']:.3f}, probability drop {evidence_row['probability_drop']:.4f}: {evidence_row['evidence']}")
-        lines.extend(["", "## Transcript", "", item["transcript"] or "(not supplied)", "", "## Interpretation note", "", "Occlusion scores describe how this trained model responds to removing features. They are not causal effects or proof that the signal expresses the named emotion.", ""])
-        safe = "".join(c if c.isalnum() or c in "-_" else "_" for c in str(item["case_id"]))
-        (cards / f"{safe}.md").write_text("\n".join(lines), encoding="utf-8")
-    confidence = np.asarray([max(row["p_negative"], row["p_neutral"], row["p_positive"]) for row in explanations])
-    representative = explanations[int(np.argmin(np.abs(confidence - np.median(confidence))))]
-    source = cards / ("".join(c if c.isalnum() or c in "-_" else "_" for c in str(representative["case_id"])) + ".md")
-    representative_card = out_dir / "typical_explanation_card.md"
-    representative_card.write_text(source.read_text(encoding="utf-8"), encoding="utf-8")
-    return str(representative["case_id"])
-
-
-def _plot_validation(out_dir: Path, y_cls: np.ndarray, prediction: dict[str, np.ndarray]) -> None:
-    pred_cls = prediction["logits"].argmax(axis=-1)
-    matrix = confusion_matrix(y_cls, pred_cls, labels=[0, 1, 2])
-    fig, axes = plt.subplots(1, 2, figsize=(10, 4), constrained_layout=True)
-    image = axes[0].imshow(matrix, cmap="Blues")
-    axes[0].set_xticks(range(3), CLASS_NAMES, rotation=15)
-    axes[0].set_yticks(range(3), CLASS_NAMES)
-    axes[0].set_xlabel("Predicted")
-    axes[0].set_ylabel("True")
-    axes[0].set_title("Validation confusion matrix")
-    for (i, j), value in np.ndenumerate(matrix):
-        axes[0].text(j, i, str(value), ha="center", va="center")
-    fig.colorbar(image, ax=axes[0], fraction=0.046)
-    axes[1].scatter(prediction["intensity"], prediction["true_sentiment"], s=12, alpha=0.55)
-    axes[1].plot([-3, 3], [-3, 3], color="gray", linestyle="--", linewidth=1)
-    axes[1].set(xlim=(-3, 3), ylim=(-3, 3), xlabel="Predicted sentiment", ylabel="True sentiment", title="Validation intensity")
-    fig.savefig(out_dir / "validation_diagnostics.png", dpi=180)
-    plt.close(fig)
-
-
-def run(args: argparse.Namespace) -> None:
-    out_dir = args.output_dir.expanduser().resolve()
-    out_dir.mkdir(parents=True, exist_ok=True)
-    started = time.time()
-    model, training_info, validation = _train(args, out_dir)
-    _plot_validation(out_dir, validation["y_cls"], {**validation["prediction"], "true_sentiment": validation["y_reg"]})
-
-    tokenizer = AutoTokenizer.from_pretrained(TEXT_MODEL_ID, use_fast=True)
-    with np.load(out_dir / "preprocessor.npz", allow_pickle=False) as saved:
-        stats = {m: {key: saved[f"{m}_{key}"].astype(np.float32) for key in ("mean", "std")} for m in MODALITIES}
-    cases, input_locations = _read_attachment4(args.attachment4_version)
-    device = next(model.parameters()).device
-    explanation_rows, all_local = [], []
-    prediction_rows = []
-    for case in cases:
-        explanation, local = _explain_case(model, case, stats, tokenizer, device, args.explanation_batch_size)
-        explanation_rows.append(explanation)
-        all_local.extend(local)
-        prediction_rows.append({key: explanation[key] for key in (
-            "case_id", "predicted_class", "predicted_class_name", "predicted_sentiment",
-            "p_negative", "p_neutral", "p_positive", "source_video",
-        )})
-    _write_csv(out_dir / "attachment4_predictions.csv", prediction_rows)
-    _write_csv(out_dir / "attachment4_explanations.csv", explanation_rows)
-    _write_csv(out_dir / "attachment4_local_evidence.csv", all_local)
-    _write_csv(out_dir / "attachment4_input_audit.csv", [case["input_audit"] for case in cases])
-    typical_id = _write_cards(out_dir, {case["case_id"]: case for case in cases}, explanation_rows, all_local)
-    manifest = {
-        "created_at_unix": time.time(),
-        "elapsed_seconds": time.time() - started,
-        "seed": SEED,
-        "training_input": training_info,
-        "attachment4": input_locations,
-        "attachment4_version": args.attachment4_version,
-        "attachment4_cases": len(cases),
-        "adapter": "Q1AlignmentAdapter shared relative-progress projection",
-        "coordinate_limit": "source-time stamps are absent; local audio/video positions are normalized progress, not seconds",
-        "model": "EarlyConcat + BiGRU",
-        "explanation": "single-modality and single-slot occlusion probability drops; model sensitivity, not causal attribution",
-        "validation_metrics": validation["metrics"],
-        "typical_explanation_case": typical_id,
-        "outputs": [
-            "model_best.pt", "preprocessor.npz", "validation_metrics.json", "validation_predictions.csv",
-            "validation_errors.csv", "validation_diagnostics.png", "attachment4_predictions.csv",
-            "attachment4_explanations.csv", "attachment4_local_evidence.csv", "attachment4_input_audit.csv", "typical_explanation_card.md",
-        ],
-    }
-    _write_json(out_dir / "run_manifest.json", manifest)
-    print(f"Q3 complete: {len(cases)} Attachment 4 predictions saved under {out_dir}", flush=True)
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--input-version", choices=("aligned_50", "unaligned_50"), default="unaligned_50")
-    parser.add_argument("--attachment4-version", choices=("unaligned_50",), default="unaligned_50")
-    parser.add_argument("--data-path", type=Path, default=None, help="Optional explicit Attachment 2 pickle path")
-    parser.add_argument("--output-dir", type=Path, default=PROJECT_ROOT / "output" / "q3")
-    parser.add_argument("--device", choices=("auto", "cpu", "cuda"), default="auto")
-    parser.add_argument("--epochs", type=int, default=12)
-    parser.add_argument("--patience", type=int, default=3)
-    parser.add_argument("--batch-size", type=int, default=64)
-    parser.add_argument("--learning-rate", type=float, default=3e-4)
-    parser.add_argument("--weight-decay", type=float, default=1e-3)
-    parser.add_argument("--explanation-batch-size", type=int, default=32)
-    args = parser.parse_args()
-    run(args)
+from .run_experiments import main
 
 
 if __name__ == "__main__":