{"id":"W2798202392","doi":"10.1139/cjfas-2017-0204","title":"Using hierarchical models to estimate stock-specific and seasonal variation in ocean distribution, survivorship, and aggregate abundance of fall run Chinook salmon","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Navy","keywords":"Chinook wind; Oncorhynchus; Fishery; Stock (firearms); Stock assessment; Juvenile; Geography; Fisheries management; Oceanography; Environmental science; Ecology; Biology; Fishing; Fish <Actinopterygii>; Geology","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":[0.002051471,0.000613571,0.0005627954,0.0009184281,0.0006543629,0.001079569,0.001661465,0.0007611953,0.001693778],"category_scores_gemma":[0.00492934,0.0007188486,0.0009783757,0.0009866891,0.0007079813,0.001274505,0.001061046,0.00103695,0.0002719686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002276877,"about_ca_system_score_gemma":0.001581343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1276313,"about_ca_topic_score_gemma":0.1711665,"domain_scores_codex":[0.9993556,0.0002701027,0.00003696512,0.0001902873,0.00005049753,0.0000966079],"domain_scores_gemma":[0.9975024,0.00159186,0.000417458,0.0001823256,0.0001726589,0.0001332252],"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":[0.00005968845,0.00006889224,0.03467648,0.00002023077,0.0001932584,0.00004356331,0.0001190415,0.952748,0.0003501637,0.004672442,0.0004134031,0.006634837],"study_design_scores_gemma":[0.000008319418,0.0000125893,0.003186981,0.000002551776,0.00001739233,0.000005221832,0.00002061263,0.9936406,0.00004234114,0.002948486,0.0001082459,0.000006653802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7090366,0.0002539552,0.284869,0.0004780753,0.00003653171,0.00008967721,0.001988312,0.000710499,0.002537431],"genre_scores_gemma":[0.9667652,0.0001129513,0.02906144,0.00006774063,0.00002520858,0.0001020463,0.001309042,0.0000600664,0.002496209],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1276313,"threshold_uncertainty_score":0.2537768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02840708450245662,"score_gpt":0.2434252624485437,"score_spread":0.2150181779460871,"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."}}