{"id":"W2092145088","doi":"10.1111/j.1539-6924.2009.01217.x","title":"The Influence of Weather Conditions on the Relative Incident Rate of Fishing Vessels","year":2009,"lang":"en","type":"article","venue":"Risk Analysis","topic":"Maritime Navigation and Safety","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"National Oceanic and Atmospheric Administration; Virginia Commonwealth University; National Science Foundation","keywords":"Environmental science; Fishing; Meteorology; Coast guard; Regression analysis; Automatic weather station; Climatology; Geography; Statistics; Fishery; Geology; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003846142,0.00005655792,0.0001259561,0.00006230108,0.000108693,0.00001260298,0.0001184143,0.0000277021,0.00009513184],"category_scores_gemma":[0.0001707063,0.0000336279,0.0001266059,0.0006083316,0.00004120119,0.00006542616,0.000006693568,0.0001299287,0.000009153048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001820409,"about_ca_system_score_gemma":0.000005486623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009219511,"about_ca_topic_score_gemma":0.00004660438,"domain_scores_codex":[0.9994541,0.00009931384,0.0002151692,0.00005780551,0.0001056877,0.0000678973],"domain_scores_gemma":[0.9990754,0.0005192414,0.00009944369,0.0002214547,0.00006707738,0.00001741702],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.000005699242,0.00001514778,0.007386733,0.000002912678,0.0006582233,4.650765e-7,0.0009750415,0.9725823,0.001303485,0.01586316,0.0003278252,0.0008789815],"study_design_scores_gemma":[0.00009801061,0.00001905137,0.9268703,0.00002409308,0.0005635761,1.217516e-7,0.0001861532,0.05887694,0.003187809,0.009585706,0.0005122781,0.00007592029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9944263,0.00006699035,0.001269702,0.000492651,0.000008341601,0.00005758526,0.00003186363,0.00001969157,0.003626906],"genre_scores_gemma":[0.9995299,0.0002496162,0.00003289772,0.00005098782,0.000006248107,0.000003389144,0.000006006162,0.000003109766,0.0001178447],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9194836,"threshold_uncertainty_score":0.1371306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005363744723055822,"score_gpt":0.2298366186340442,"score_spread":0.2244728739109884,"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."}}