{"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":"codex-gemma-dda1882f352a","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.007943556,0.0001741732,0.0006642145,0.002058619,0.0009593882,0.0003906293,0.001906446,0.00006276275,0.000912322],"category_scores_gemma":[0.005722213,0.0001179744,0.0004420461,0.02231218,0.001356037,0.001649665,0.0002136409,0.0001746644,0.0002303237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006150283,"about_ca_system_score_gemma":0.0002656709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00161158,"about_ca_topic_score_gemma":0.0006164695,"domain_scores_codex":[0.994484,0.0003665529,0.001178897,0.0009455994,0.002616591,0.000408361],"domain_scores_gemma":[0.9954742,0.001052418,0.0004114236,0.001191184,0.001637606,0.0002332206],"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":[0.00003836187,0.0001631087,0.1526866,0.000002825334,0.0004846422,8.749364e-7,0.006764723,0.7241195,0.002042593,0.04369676,0.0001348693,0.06986511],"study_design_scores_gemma":[0.00007633994,0.00003842798,0.2098892,0.000003285101,0.000239494,4.698318e-7,0.005801218,0.7750428,0.0001283562,0.008559502,0.00008899171,0.0001319505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.282434,0.0001850237,0.7032102,0.0008110394,0.00009962278,0.0002695251,0.00005925378,0.0000268361,0.01290452],"genre_scores_gemma":[0.9306868,0.0001899442,0.06870171,0.00005190988,0.00001272847,0.00001406379,0.000008653804,0.000004905653,0.0003292669],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6482528,"threshold_uncertainty_score":0.9989287,"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."}}