{"id":"W2083581587","doi":"10.1016/j.ssci.2010.02.013","title":"Human error risk analysis in offshore emergencies","year":2010,"lang":"en","type":"article","venue":"Safety Science","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":93,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Human error; Risk analysis (engineering); Accidental; Work (physics); Risk assessment; Near miss; Human factors and ergonomics; Submarine pipeline; Occupational safety and health; Poison control; Forensic engineering; Process (computing); Engineering; Computer science; Medical emergency; Computer security; Business; Medicine","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":["bibliometrics","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01526054,0.0002018204,0.0005479985,0.002591919,0.001247596,0.00028338,0.003190677,0.0001012342,0.003247476],"category_scores_gemma":[0.004956165,0.0001446713,0.0004455852,0.02514664,0.001542916,0.001006703,0.0003829875,0.000549665,0.0005171088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005326571,"about_ca_system_score_gemma":0.0002300746,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00127127,"about_ca_topic_score_gemma":0.03962703,"domain_scores_codex":[0.9937824,0.0002115555,0.001128762,0.001173265,0.003056629,0.0006473886],"domain_scores_gemma":[0.9965976,0.0003882931,0.0004163433,0.001752113,0.0005417919,0.0003038504],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001282776,0.00007248551,0.9500325,5.704227e-7,0.00004293156,0.000007619048,0.001489223,0.007292303,0.006865537,0.006854135,0.0003045296,0.02702531],"study_design_scores_gemma":[0.0001200769,0.00002057255,0.9406155,0.000001428124,0.00009392599,0.000001170245,0.001323379,0.02295139,0.0003160812,0.03012779,0.004219276,0.00020944],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9780577,0.00004176765,0.002447807,0.001649874,0.0004757569,0.0001075946,0.00003275991,0.00003903143,0.01714768],"genre_scores_gemma":[0.9965146,0.00004787146,0.001718131,0.00007622286,0.00006095307,0.000004814412,0.000002982812,0.000005545815,0.001568893],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03835576,"threshold_uncertainty_score":0.9976637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05195350049078643,"score_gpt":0.3927848070314119,"score_spread":0.3408313065406254,"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."}}