{"id":"W2804155484","doi":"10.1139/cjfas-2017-0382","title":"A Bayesian life-cycle model to estimate escapement at maximum sustained yield in salmon based on limited information","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Park Service","keywords":"Escapement; Oncorhynchus; Abundance (ecology); Maximum sustainable yield; Bayesian probability; Population; Environmental science; Life history; Fishery; Statistics; Fisheries management; Ecology; Biology; Mathematics; Fish <Actinopterygii>; Fishing; Demography","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":[0.005644223,0.001003137,0.001556099,0.001387773,0.0006929774,0.001352345,0.002725339,0.001608453,0.002494369],"category_scores_gemma":[0.01244424,0.001291408,0.001224927,0.001363786,0.001092555,0.001690099,0.001228688,0.001555491,0.0005286736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001960547,"about_ca_system_score_gemma":0.00220238,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04302105,"about_ca_topic_score_gemma":0.03537048,"domain_scores_codex":[0.9989756,0.0004852405,0.00005731919,0.0002453815,0.0001134404,0.0001229819],"domain_scores_gemma":[0.9956209,0.003125316,0.0005625502,0.0001419937,0.0003815916,0.0001676641],"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":[0.0001120059,0.00005354678,0.006884939,0.00003996357,0.0001194231,0.00005731007,0.00009632181,0.9677816,0.000377865,0.01328633,0.000638283,0.01055236],"study_design_scores_gemma":[0.00001279897,0.00001799898,0.0009390776,0.000007952999,0.00001751469,0.00001075542,0.000006745395,0.9944481,0.00003785826,0.004309873,0.0001769198,0.0000144396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1824553,0.0006005439,0.8121091,0.0006522626,0.00005813559,0.0001659374,0.0009993251,0.0003373673,0.002621937],"genre_scores_gemma":[0.8875498,0.0006052813,0.1021811,0.0002504457,0.00007786629,0.0005912561,0.00209259,0.0001333051,0.006518425],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.956979,"threshold_uncertainty_score":0.08554131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01915434271917299,"score_gpt":0.2453362628561158,"score_spread":0.2261819201369428,"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."}}