{"id":"W4317353321","doi":"10.1139/cjfas-2022-0219","title":"Age structure augments the predictive power of time series for fisheries and conservation","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Southwest Fisheries Science Center; National Oceanic and Atmospheric Administration","keywords":"Abundance (ecology); Time series; Predictive power; Series (stratigraphy); Limiting; Econometrics; Ecology; Sustainability; Index (typography); Computer science; Fishery; Environmental science; Statistics; Mathematics; Biology; Engineering","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":[0.003792697,0.0005760923,0.0005785771,0.001590008,0.0003985665,0.001297263,0.0005939028,0.0008824155,0.002681431],"category_scores_gemma":[0.01757362,0.00026715,0.0007314723,0.001138381,0.0003183836,0.003349268,0.0007865596,0.001262757,0.0005589811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006136661,"about_ca_system_score_gemma":0.0005745304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01249638,"about_ca_topic_score_gemma":0.01593936,"domain_scores_codex":[0.9995371,0.0001691791,0.00003870788,0.0001349917,0.00007462293,0.0000454067],"domain_scores_gemma":[0.9893607,0.007692002,0.0009983606,0.001071446,0.0006582732,0.0002192277],"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":[0.0004670687,0.000239803,0.2412189,0.0001885748,0.0002665997,0.0001758002,0.0002817508,0.5110111,0.00313531,0.01124408,0.00326244,0.2285086],"study_design_scores_gemma":[0.00001418017,0.00007928321,0.03941437,0.00005698369,0.00007121039,0.00004501385,0.00004756174,0.9457574,0.0006972752,0.01115702,0.002623312,0.00003639236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7523249,0.002555738,0.2244578,0.002466658,0.0006364206,0.0001008511,0.003000861,0.001199043,0.01325787],"genre_scores_gemma":[0.9746278,0.0005066493,0.02260068,0.0001178288,0.0001474418,0.00002323658,0.0009073779,0.00006007679,0.001008889],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01249638,"threshold_uncertainty_score":0.02484727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01742205652486531,"score_gpt":0.2286422758016433,"score_spread":0.211220219276778,"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."}}