{"id":"W3094841544","doi":"10.1016/j.jenvman.2020.111520","title":"Implementing Bayesian networks for ISO 31000:2018-based maritime oil spill risk management: State-of-art, implementation benefits and challenges, and future research directions","year":2020,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Oil Spill Detection and Mitigation","field":"Environmental Science","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Strategic Research Council; HELSUS Kestävyystieteen Instituutti; Genome Institute of Singapore; Marine Environmental Observation Prediction and Response Network; Helsingin Yliopisto","keywords":"Bayesian network; Oil spill; Preparedness; Risk analysis (engineering); DPSIR; Risk management; Risk assessment; Scale (ratio); Environmental resource management; Environmental science; Probabilistic logic; Environmental planning; Computer science; Business; Environmental engineering; Geography; Computer security","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":[0.001120392,0.0001950452,0.0002215285,0.0001597204,0.0003681907,0.0000517571,0.000153184,0.00004715669,0.0004938851],"category_scores_gemma":[0.000005298059,0.0001943864,0.00009457475,0.000171958,0.0001432255,0.0002794841,0.0003179884,0.000209148,0.00001054277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001890383,"about_ca_system_score_gemma":0.000002790315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001680165,"about_ca_topic_score_gemma":0.00009724617,"domain_scores_codex":[0.9979437,0.0001431023,0.0005845008,0.000366133,0.0005423389,0.0004202194],"domain_scores_gemma":[0.9991339,0.00005907326,0.0004284769,0.0001469643,0.000009851678,0.0002217051],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001266985,0.00009887953,0.003793156,0.0001535321,0.000132007,0.000008331121,0.0003221443,0.0007779849,0.0001987739,0.0002535148,0.0006349679,0.9935],"study_design_scores_gemma":[0.004842971,0.001642534,0.3287408,0.0001073885,0.0003827657,0.00001741213,0.006928415,0.00557017,0.0007141855,0.0007027811,0.6498764,0.0004741679],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8288362,0.06703947,0.02550173,0.02634766,0.002037601,0.006899891,0.0007902745,0.0001967335,0.0423504],"genre_scores_gemma":[0.8295804,0.159546,0.009939749,0.0003113667,0.0002474754,0.00006897868,0.00003290414,0.00004806921,0.0002249623],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9930258,"threshold_uncertainty_score":0.792685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01991006481548811,"score_gpt":0.2563914922986912,"score_spread":0.2364814274832031,"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."}}