{"id":"W4393256508","doi":"10.1016/j.ress.2024.110104","title":"An operational risk management approach for small fishing vessel","year":2024,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Excellence Research Chairs, Government of Canada","keywords":"Risk analysis (engineering); Human error; Risk management; Bayesian network; Domain (mathematical analysis); Field (mathematics); Safeguarding; Fishing; Engineering; Computer science; Operations research; Business","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":[0.00934735,0.0002672325,0.000485929,0.0003060979,0.0003041611,0.000727241,0.0008291294,0.0001356839,0.00002653753],"category_scores_gemma":[0.0006689659,0.0002063955,0.0004095208,0.0009799706,0.00002874934,0.000500601,0.0000741899,0.0002453459,0.00006597705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003315499,"about_ca_system_score_gemma":0.00005544374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008341914,"about_ca_topic_score_gemma":0.000005616737,"domain_scores_codex":[0.9962215,0.0002352602,0.001142936,0.001088879,0.0009382952,0.0003731569],"domain_scores_gemma":[0.9970207,0.001268472,0.00009753586,0.001179972,0.0002446007,0.0001886982],"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":[0.00003353646,0.00005006712,0.0007450756,0.0005358372,0.0001157994,0.000004476153,0.000324728,0.955267,0.00003948621,0.0227853,0.0005992361,0.01949952],"study_design_scores_gemma":[0.0001713087,0.00003325273,0.004595635,0.0000801356,0.0001043485,0.000006802533,0.0006125203,0.9563735,0.00002479506,0.0008359856,0.03691494,0.0002467693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01253813,0.000457241,0.9822145,0.0003567532,0.0007448114,0.0006964548,0.0001967788,0.0004599511,0.002335405],"genre_scores_gemma":[0.8794894,0.00008768465,0.119417,0.00001268095,0.0002956971,0.0001505422,0.00008040947,0.00003426746,0.0004322953],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8669513,"threshold_uncertainty_score":0.8416564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02631876755252753,"score_gpt":0.288611259837782,"score_spread":0.2622924922852544,"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."}}