{"id":"W4412877242","doi":"10.1145/3711896.3736999","title":"Hyperparametric Influence Minimization: Feature-Driven Intervention Beyond Blocking","year":2025,"lang":"en","type":"article","venue":"","topic":"Data Analysis with R","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; National Research Foundation","keywords":"Blocking (statistics); Minification; Computer science; Feature (linguistics); Intervention (counseling); Computer network; Psychology; World Wide Web","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001687646,0.0001106977,0.0001505126,0.0006024946,0.0001012136,0.0003459667,0.001130557,0.00005864486,0.0000326878],"category_scores_gemma":[0.0002143138,0.00009974278,0.0001221291,0.003294381,0.00002778872,0.0008482214,0.0004861569,0.0001068121,0.00008042437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005191143,"about_ca_system_score_gemma":0.00004215305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004875078,"about_ca_topic_score_gemma":0.0000376039,"domain_scores_codex":[0.9988955,0.00005988886,0.0002300585,0.0003996463,0.0002365917,0.0001783546],"domain_scores_gemma":[0.9988984,0.000101341,0.00008831947,0.0007086264,0.0001574673,0.00004580792],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001498489,0.0004625346,0.0795145,0.0001181291,0.0005236806,0.00004132072,0.0003716826,0.02717319,0.0007366426,0.4358808,0.1097884,0.3453741],"study_design_scores_gemma":[0.001179993,0.0001009376,0.09313875,0.0002034879,0.0001599018,0.00002569415,0.0000761944,0.8522101,0.005374571,0.00704335,0.03986683,0.0006202043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01328817,0.0002213531,0.9728667,0.001988632,0.0002677485,0.0001096263,0.000002273301,0.0002037099,0.01105181],"genre_scores_gemma":[0.8177069,0.00001311104,0.1732004,0.001257128,0.00002053136,0.00001301335,0.00001719192,0.000004528051,0.007767218],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8250369,"threshold_uncertainty_score":0.4067393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006252236179735615,"score_gpt":0.2571757392529401,"score_spread":0.2509235030732044,"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."}}