{"id":"W4388723825","doi":"10.1139/cjfas-2023-0169","title":"Evaluating robustness of harvest control rules to climate-driven variability in Pacific sardine recruitment","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":"Sardine; Environmental science; Pelagic zone; Fishing; Fishery; Stock assessment; Climate change; Fisheries management; Biomass (ecology); Ecology; Biology; 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":[0.01822677,0.000568751,0.000411531,0.0007250911,0.0004037453,0.001064469,0.0007636524,0.0008484946,0.000548565],"category_scores_gemma":[0.05222815,0.0002924783,0.001042825,0.0003948723,0.0007604604,0.001008322,0.0005742966,0.0007889535,0.0001199792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00152776,"about_ca_system_score_gemma":0.0008617399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01402154,"about_ca_topic_score_gemma":0.006356272,"domain_scores_codex":[0.9960681,0.00159661,0.0004077204,0.001052787,0.0005739261,0.0003008212],"domain_scores_gemma":[0.9293034,0.05552341,0.006956378,0.003797104,0.003616173,0.0008035538],"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":[0.003516827,0.0009274998,0.2860626,0.00009811773,0.001257558,0.0001528925,0.000207781,0.6756132,0.005071584,0.0006713852,0.0003875337,0.02603296],"study_design_scores_gemma":[0.000224606,0.005330441,0.2256469,0.00003280583,0.000496153,0.0001140876,0.0002245447,0.759769,0.006494964,0.0009596418,0.0006213706,0.00008544404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974449,0.00004164671,0.001790408,0.00003142157,0.000009563061,0.00004937332,0.0001191765,0.0000273299,0.000486253],"genre_scores_gemma":[0.9986591,0.00001305536,0.0009001338,0.00002327102,0.00000447244,0.0000300074,0.0002595059,0.000005831971,0.0001044773],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01822677,"threshold_uncertainty_score":0.09639347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08708601032260715,"score_gpt":0.3171792993094018,"score_spread":0.2300932889867947,"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."}}