{"id":"W3136073513","doi":"10.1111/faf.12550","title":"The longer the better? Trade‐offs in fisheries stock assessment in dynamic ecosystems","year":2021,"lang":"en","type":"article","venue":"Fish and Fisheries","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Memorial University of Newfoundland","funders":"","keywords":"Population; Stock (firearms); Stock assessment; Vital rates; Fish stock; Population size; Ecosystem; Population dynamics of fisheries; Econometrics; Environmental science; Population model; Ecology; Fishery; Statistics; Biology; Fish <Actinopterygii>; Population growth; Economics; Mathematics; Geography; Fishing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02263519,0.000392956,0.0005495929,0.00115673,0.0007211139,0.00311354,0.0009231257,0.001475542,0.002800268],"category_scores_gemma":[0.07973261,0.0003044458,0.0006397887,0.001140818,0.001107775,0.0056477,0.002013202,0.001196727,0.0001992644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001520194,"about_ca_system_score_gemma":0.0008621468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003979559,"about_ca_topic_score_gemma":0.009097043,"domain_scores_codex":[0.9961343,0.002321573,0.0002953729,0.0005148801,0.0005204205,0.0002135294],"domain_scores_gemma":[0.9403846,0.0496031,0.004638051,0.0020556,0.002178221,0.001140332],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001678932,0.0005639486,0.3672988,0.0003985536,0.0008871379,0.0003911939,0.001177203,0.3401356,0.003250855,0.05780344,0.001994447,0.22442],"study_design_scores_gemma":[0.0001142875,0.001071103,0.1259773,0.0005329489,0.0003137845,0.0003677461,0.001940132,0.7539904,0.003064933,0.1090894,0.003378336,0.0001597151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9297013,0.002114164,0.0557432,0.004830043,0.00009763917,0.00007214444,0.0003718499,0.0000675253,0.007002162],"genre_scores_gemma":[0.9921293,0.0001624393,0.007042257,0.0001515933,0.00001824978,0.00001772994,0.0000513679,0.00001171873,0.0004153822],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02263519,"threshold_uncertainty_score":0.1197078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01408395159875769,"score_gpt":0.2407017836119566,"score_spread":0.2266178320131989,"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."}}