{"id":"W2594723946","doi":"10.1111/risa.12736","title":"A Flexible Hierarchical Bayesian Modeling Technique for Risk Analysis of Major Accidents","year":2017,"lang":"en","type":"article","venue":"Risk Analysis","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Lloyd's Register Foundation","keywords":"Bayesian probability; Computer science; Risk analysis (engineering); Econometrics; Artificial intelligence; Mathematics; Business","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":["metaresearch","metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.009762432,0.0004084624,0.002487726,0.005993719,0.002010385,0.0007423931,0.00346497,0.0003239389,0.0007349821],"category_scores_gemma":[0.008754862,0.0003134675,0.005668437,0.009001438,0.0002668528,0.0006551273,0.0004714614,0.0004339088,0.0000475154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007768229,"about_ca_system_score_gemma":0.0001074511,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008333706,"about_ca_topic_score_gemma":0.008937111,"domain_scores_codex":[0.9927529,0.0007346019,0.002197253,0.00144883,0.002231208,0.00063521],"domain_scores_gemma":[0.9895773,0.001667182,0.002751889,0.004534052,0.001101822,0.0003677848],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001764095,0.0001361268,0.5961721,0.000003984222,0.02172136,0.000003881997,0.000295451,0.3509776,0.0001592083,0.0003664466,0.0001550781,0.02983234],"study_design_scores_gemma":[0.0003636911,0.00003968786,0.07828663,0.000006079435,0.05373823,3.333522e-7,0.0002439467,0.8125036,0.0007290439,0.05361554,0.0001588278,0.0003144372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1597662,0.0001775756,0.8380584,0.0004261044,0.0000489183,0.0003081527,0.00055767,0.00004238971,0.0006146076],"genre_scores_gemma":[0.9642881,0.001114642,0.0332305,0.00002506069,0.00008063404,0.0001032968,0.00007556919,0.00002549148,0.001056751],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8048279,"threshold_uncertainty_score":0.9999318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07046246759926908,"score_gpt":0.4108300358596174,"score_spread":0.3403675682603483,"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."}}