{"id":"W3087259180","doi":"10.1016/j.psep.2020.09.038","title":"Review and analysis of supervised machine learning algorithms for hazardous events in drilling operations","year":2020,"lang":"en","type":"article","venue":"Process Safety and Environmental Protection","topic":"Drilling and Well Engineering","field":"Engineering","cited_by":116,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Machine learning; Artificial intelligence; Algorithm; Computer science; Drilling engineering; Deep learning; Artificial neural network; Support vector machine; Drilling; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.00009935749,0.0000905287,0.0002082932,0.00005853432,0.00005745995,0.000004076134,0.00002391856,0.00003526833,0.000007606744],"category_scores_gemma":[0.00001474509,0.00009457117,0.00003111546,0.0001897149,0.00001087325,0.00009731387,0.00001030275,0.0001086195,3.218898e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002032037,"about_ca_system_score_gemma":0.00000134374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001444559,"about_ca_topic_score_gemma":0.000006146201,"domain_scores_codex":[0.9995306,0.000009875163,0.0001903025,0.0001346607,0.00004789226,0.00008669565],"domain_scores_gemma":[0.9999014,0.000008845706,0.00001584771,0.00003157088,0.000002858425,0.00003952191],"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":[0.00001149279,0.000007289377,0.0008466752,0.001278832,0.00007735973,8.45756e-8,0.0002930304,0.9869993,0.001298613,9.994141e-7,8.106252e-8,0.009186201],"study_design_scores_gemma":[0.0002447393,0.00004097708,0.0009865706,0.0001100181,0.0001901137,0.000001061225,0.00005210525,0.9975526,0.0005756682,0.000005334534,0.0001431513,0.00009767718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05669865,0.01884414,0.9234269,0.0001621729,0.00002388037,0.0006918669,0.00004809456,0.00008539552,0.00001893681],"genre_scores_gemma":[0.9846954,0.01471982,0.0003965546,0.00002695172,0.00001129602,0.0000571102,0.00007496694,0.00001418443,0.000003726907],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9279968,"threshold_uncertainty_score":0.38565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01270698404148379,"score_gpt":0.2064528430223266,"score_spread":0.1937458589808428,"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."}}