{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004687564,0.00009290536,0.0002387527,0.0004783704,0.0000359983,0.000007464505,0.00006164683,0.00007558776,0.0001059369],"category_scores_gemma":[0.00003651812,0.00009603778,0.0002049606,0.0008825723,0.00001276025,0.00004138913,0.000009301756,0.00009228318,0.00001868127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001189438,"about_ca_system_score_gemma":0.00000648337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003119335,"about_ca_topic_score_gemma":0.0004525894,"domain_scores_codex":[0.9991756,0.00008273657,0.0003151493,0.000156915,0.0001202993,0.0001492767],"domain_scores_gemma":[0.999545,0.0001650632,0.00006720932,0.0001780796,0.000008274827,0.0000363681],"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":[0.00001006189,0.00001411309,0.04546169,0.00001937893,0.0003645533,5.958886e-7,0.0001475714,0.9446715,0.0003117305,0.000004928883,0.00004376203,0.008950136],"study_design_scores_gemma":[0.000429398,0.000008218753,0.1171629,0.000004002914,0.0006323765,5.466464e-8,0.00006882528,0.8802418,0.0006696514,0.0002834995,0.0004270362,0.00007217708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.174829,0.00002409806,0.8242594,0.00002647578,0.00004138995,0.0001537234,0.0004643742,0.000120303,0.00008125544],"genre_scores_gemma":[0.9889876,0.000156097,0.0101892,0.000005464035,0.000006301574,0.00003859778,0.0004829655,0.00001692101,0.0001168705],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8141586,"threshold_uncertainty_score":0.3916307,"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."}}