{"id":"W2019509828","doi":"10.1016/j.ssci.2013.01.022","title":"Quantitative risk analysis of offshore drilling operations: A Bayesian approach","year":2013,"lang":"en","type":"article","venue":"Safety Science","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":397,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University; Memorial University of Newfoundland","funders":"","keywords":"Bayesian network; Bow tie; Fault tree analysis; Event tree; Accident analysis; Offshore drilling; Computer science; Bayesian probability; Conditional probability; Accident (philosophy); Tree (set theory); Data mining; Engineering; Reliability engineering; Drilling; Machine learning; Artificial intelligence; Statistics","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":[0.01063969,0.001455003,0.002576916,0.003696787,0.0007151656,0.003600654,0.00242631,0.00224563,0.002517199],"category_scores_gemma":[0.03479051,0.001517887,0.001877657,0.001917052,0.002450789,0.004172776,0.002231971,0.002466171,0.0002888244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002212554,"about_ca_system_score_gemma":0.002303895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005466482,"about_ca_topic_score_gemma":0.004146002,"domain_scores_codex":[0.9940731,0.003513139,0.0002453304,0.0004820276,0.001454391,0.0002320406],"domain_scores_gemma":[0.9754128,0.02120174,0.001392151,0.0006111563,0.001095002,0.0002870744],"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":[0.00005659214,0.00007942473,0.0009705311,0.0001487184,0.0001562496,0.00006600637,0.00009264886,0.803941,0.0005342053,0.1658306,0.0007242931,0.0273998],"study_design_scores_gemma":[0.00001954317,0.00003533335,0.0004294343,0.00005361346,0.00004833481,0.00004376906,0.00002400133,0.7791615,0.0001929,0.2193109,0.0006517283,0.00002901435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008473575,0.000425711,0.9883453,0.0005184245,0.0000156348,0.00003365678,0.00008274897,0.00004262514,0.002062338],"genre_scores_gemma":[0.7059044,0.003256508,0.2843068,0.0003765011,0.0003765984,0.0004480732,0.000426412,0.0001141633,0.00479071],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01063969,"threshold_uncertainty_score":0.05626869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0596678224890763,"score_gpt":0.3655826845139191,"score_spread":0.3059148620248427,"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."}}