{"id":"W4416330618","doi":"10.3791/69125","title":"Ensemble of Temporal Weighting, Causal Inference, and Hierarchical Attribution towards SHAP Optimization","year":2025,"lang":"en","type":"article","venue":"Journal of Visualized Experiments","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Oakville-Trafalgar Memorial Hospital","funders":"Fundação para a Ciência e a Tecnologia","keywords":"Interpretability; Weighting; Outlier; Causal inference; Normalization (sociology); Preprocessor; Boosting (machine learning); Bayesian probability","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006310728,0.0001440732,0.0003512323,0.0003648216,0.0001133318,0.0001211233,0.0004488583,0.0000969791,0.00003109128],"category_scores_gemma":[0.0002212394,0.0001271057,0.00008460399,0.0005156554,0.00009147808,0.0007223771,0.0002319453,0.000175902,0.000002105526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000743524,"about_ca_system_score_gemma":0.0002792438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006084198,"about_ca_topic_score_gemma":0.000001784555,"domain_scores_codex":[0.9981613,0.0001772141,0.0007999102,0.0002021747,0.0004297401,0.0002297192],"domain_scores_gemma":[0.998658,0.00009676511,0.000489313,0.0002019178,0.0004415408,0.000112464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009341899,0.002189589,0.02040382,0.0001928533,0.0004761408,0.0001389001,0.009798273,0.007600259,0.3010467,0.6013973,0.002041831,0.05378015],"study_design_scores_gemma":[0.001086893,0.0006743179,0.001023908,0.0002662343,0.0000246017,0.0000276607,0.0002582342,0.1399954,0.8491759,0.005773554,0.001486392,0.0002068983],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1908202,0.0003961749,0.8072954,0.0002821607,0.0003737719,0.0001115953,0.000001021536,0.0000185136,0.0007011011],"genre_scores_gemma":[0.9174756,0.00008987442,0.08216227,0.0001093105,0.00004516356,0.00000430885,0.000001998865,0.000005861943,0.0001055624],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7266554,"threshold_uncertainty_score":0.5183219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0426314440416591,"score_gpt":0.4279417565005144,"score_spread":0.3853103124588553,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}