{"id":"W4210306762","doi":"10.1139/cjfas-2021-0287","title":"Improving forecasts of sockeye salmon (<i>Oncorhynchus nerka</i>) with parametric and nonparametric models","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Oncorhynchus; Bay; Benchmark (surveying); Nonparametric statistics; Suite; Environmental science; Parametric statistics; Fishery; Chinook wind; Forecast error; Meteorology; Econometrics; Fish <Actinopterygii>; Statistics; Geography; Mathematics; Biology; Cartography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00163756,0.0005175178,0.0002845437,0.0003570278,0.0002188889,0.0006962247,0.0003527018,0.0003072385,0.0002885418],"category_scores_gemma":[0.003755373,0.0002262097,0.0003599663,0.0002328182,0.0001571177,0.0006822892,0.0004321142,0.0006329459,0.00006768293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000713761,"about_ca_system_score_gemma":0.0009891777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09647854,"about_ca_topic_score_gemma":0.1353539,"domain_scores_codex":[0.9997181,0.00009967306,0.00002021111,0.00005792488,0.00005705964,0.00004686853],"domain_scores_gemma":[0.9989421,0.0004962083,0.000150198,0.00008450771,0.0002664449,0.0000606338],"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":[0.00004969578,0.00003785735,0.02326348,0.00001050908,0.00003774351,0.0000193018,0.00003163453,0.9632879,0.001407522,0.0001584766,0.0004820356,0.01121385],"study_design_scores_gemma":[0.000004074086,0.00003449327,0.006752457,0.000003438385,0.0000114681,0.000003944746,0.00002244146,0.9924269,0.0004630389,0.0001333656,0.000134734,0.000009644202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9823852,0.00007410779,0.01543701,0.0001805475,0.00003103358,0.00001389791,0.0003498351,0.0001975663,0.001330888],"genre_scores_gemma":[0.995634,0.00002404673,0.003877067,0.00001171801,0.000007406868,0.000004655616,0.0002197359,0.000007836219,0.0002135545],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09647854,"threshold_uncertainty_score":0.191834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01464030859389669,"score_gpt":0.1914385788465192,"score_spread":0.1767982702526225,"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."}}