{"id":"W2010794999","doi":"10.1002/cjs.11214","title":"Estimating a treatment effect under uncertainty with application to a high‐speed railway system","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Nihon University","keywords":"Average treatment effect; Treatment effect; Identification (biology); Population; Econometrics; Statistics; Statistical analysis; Statistical model; Computer science; Mathematics; Medicine; Demography; Sociology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003588845,0.0001880697,0.0003650071,0.0001575008,0.0002230559,0.00008505231,0.0001527377,0.00005239087,0.00006371506],"category_scores_gemma":[0.0008432813,0.000138175,0.00003958531,0.0002629112,0.00007739778,0.00004360916,0.000004768471,0.0001086302,0.00006180977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007681249,"about_ca_system_score_gemma":0.000465252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001508529,"about_ca_topic_score_gemma":0.003950695,"domain_scores_codex":[0.9987319,0.0001047964,0.0004972787,0.0001576292,0.0002375427,0.0002708125],"domain_scores_gemma":[0.9970897,0.001071167,0.0003449423,0.0002350805,0.0004540941,0.0008050414],"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":[0.00002744121,0.00002660822,0.0001100439,0.0000941252,0.00005885653,0.00001467355,0.0001529291,0.01960794,0.00004676427,0.9582249,0.003283044,0.01835266],"study_design_scores_gemma":[0.00616207,0.003860306,0.0117494,0.0009127228,0.001177201,0.0006807714,0.0008033029,0.7975816,0.0004451679,0.1641903,0.01126098,0.001176121],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01732072,0.000004167455,0.9805723,0.00047247,0.0001014236,0.000408054,0.0007722061,0.00002581846,0.0003228611],"genre_scores_gemma":[0.7537225,1.629076e-7,0.2459428,0.0000861541,0.00009377065,0.00002604561,0.00005840213,0.00002013723,0.00004998361],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7940346,"threshold_uncertainty_score":0.5634614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02226295843472715,"score_gpt":0.2902295152134832,"score_spread":0.267966556778756,"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."}}