{"id":"W2055567148","doi":"10.1016/j.stam.2005.07.003","title":"Predicting tragedies, accidents, errors and failures using a learning environment","year":2005,"lang":"en","type":"article","venue":"Science and Technology of Advanced Materials","topic":"Occupational Health and Safety Research","field":"Health Professions","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Atomic Energy (Canada)","funders":"","keywords":"Human error; Outcome (game theory); Component (thermodynamics); Risk analysis (engineering); Failure rate; Computer science; Reliability engineering; Engineering; Business; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027666,0.000762868,0.0004565806,0.002057276,0.0004698,0.00106288,0.0007164099,0.001355513,0.001601533],"category_scores_gemma":[0.02302513,0.0002766639,0.0004806966,0.0008905224,0.000788483,0.001959117,0.001233511,0.000871972,0.0003931269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006519846,"about_ca_system_score_gemma":0.0005687418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004306493,"about_ca_topic_score_gemma":0.003758264,"domain_scores_codex":[0.9988344,0.0004835916,0.00008855132,0.0001758618,0.0002632795,0.0001542634],"domain_scores_gemma":[0.9770722,0.01793423,0.002248468,0.001046659,0.001104597,0.0005938403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007110164,0.001223274,0.3647879,0.00005220187,0.0001380874,0.0001781891,0.0003899577,0.5452148,0.00077354,0.001651999,0.0004172569,0.08446176],"study_design_scores_gemma":[0.00002023382,0.0006378261,0.05084094,0.0000156834,0.00003501335,0.00009538424,0.0002035219,0.9405282,0.001249692,0.006160832,0.0001800552,0.00003271619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9703168,0.00004110805,0.02832611,0.00009741923,0.000006595812,0.00006211068,0.0001541926,0.0001052108,0.0008904448],"genre_scores_gemma":[0.9925183,0.00005906211,0.006652042,0.00001228883,0.00001704979,0.00003614022,0.0001789534,0.000006086866,0.000520115],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004306493,"threshold_uncertainty_score":0.01463133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03401333502941496,"score_gpt":0.4125776277536772,"score_spread":0.3785642927242623,"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."}}