{"id":"W2122918834","doi":"10.1139/f08-130","title":"Escapement goal analysis and stock reconstruction of sockeye salmon populations (Oncorhynchus nerka) using life-history models","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Alaska Department of Fish and Game; Gordon and Betty Moore Foundation","keywords":"Escapement; Oncorhynchus; Life history; Stock (firearms); Fishery; Markov chain Monte Carlo; Life history theory; Population model; Spawn (biology); Population; Econometrics; Statistics; Bayesian probability; Biology; Mathematics; Ecology; Geography; Demography; Fish <Actinopterygii>","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":[0.001212171,0.0003064286,0.0003113051,0.0007324664,0.0002481473,0.0005003919,0.0004510184,0.0003000892,0.0005831767],"category_scores_gemma":[0.003390926,0.0003179101,0.0006403658,0.0002613612,0.0003314404,0.0005311377,0.0004222559,0.0002619869,0.00009487173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001117692,"about_ca_system_score_gemma":0.0007138308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02231872,"about_ca_topic_score_gemma":0.03034782,"domain_scores_codex":[0.9998087,0.00008941855,0.0000101614,0.00004702752,0.00002342594,0.00002128451],"domain_scores_gemma":[0.9991904,0.0004360483,0.0001676674,0.00007149176,0.0000716646,0.00006261091],"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":[0.00009389988,0.00003670739,0.1267269,0.00001532305,0.0001473493,0.00006827362,0.0001215792,0.8610496,0.0009423922,0.002794107,0.0001236113,0.007880198],"study_design_scores_gemma":[0.00001056586,0.00003181205,0.01610451,0.000005067416,0.00001788855,0.00002137633,0.00004222982,0.981754,0.0002844659,0.001646311,0.00007204143,0.000009787729],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898635,0.00002529886,0.009626125,0.00004225491,0.000001370555,0.000004818145,0.00005360857,0.00001690937,0.0003660485],"genre_scores_gemma":[0.9953512,0.00002157034,0.004186324,0.000008038209,0.000001254789,0.000007440107,0.0001850372,0.000007807434,0.0002313069],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02231872,"threshold_uncertainty_score":0.04437762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07288277465648448,"score_gpt":0.2495119704621123,"score_spread":0.1766291958056279,"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."}}