{"id":"W4367318610","doi":"10.1111/risa.14149","title":"A copula‐based method of risk prediction for autonomous underwater gliders in dynamic environments","year":2023,"lang":"en","type":"article","venue":"Risk Analysis","topic":"Maritime Navigation and Safety","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Fisheries and Oceans Canada","keywords":"Copula (linguistics); Computer science; Risk analysis (engineering); Bayesian network; Inference; Risk assessment; Underwater; Machine learning; Artificial intelligence; Econometrics; Computer security; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001307052,0.001207542,0.0008647455,0.001061172,0.0004876525,0.0008633466,0.001167268,0.0008614534,0.002443819],"category_scores_gemma":[0.004416275,0.0005651768,0.001088984,0.0008384283,0.0004739205,0.000960131,0.0009231789,0.001561033,0.0005364968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007974192,"about_ca_system_score_gemma":0.001654085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01684531,"about_ca_topic_score_gemma":0.01023347,"domain_scores_codex":[0.999464,0.0001823494,0.00002893593,0.00013843,0.0001174867,0.00006877755],"domain_scores_gemma":[0.9988458,0.0006358639,0.0001289479,0.00005011081,0.0002906143,0.00004859761],"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":[0.00003715218,0.0000333212,0.001375415,0.00004673146,0.00006572116,0.00008687578,0.00006031787,0.9454412,0.001074184,0.005205022,0.0009776375,0.04559635],"study_design_scores_gemma":[0.000001334736,0.000006407875,0.0001241238,0.000003163243,0.000005374642,0.000008564036,0.000005323442,0.9985707,0.0001042468,0.00101479,0.0001520182,0.000003806546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005480455,0.0001035263,0.9934828,0.00005116044,0.00001669945,0.00002510919,0.00004039215,0.0001259593,0.0006739295],"genre_scores_gemma":[0.6374577,0.0008250246,0.3551807,0.0001641259,0.000116057,0.0003843111,0.0005225908,0.0002071246,0.005142376],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01684531,"threshold_uncertainty_score":0.03349453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006856477885121215,"score_gpt":0.2502054429182684,"score_spread":0.2433489650331471,"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."}}