{"id":"W3178805465","doi":"10.1139/cjfas-2020-0394","title":"Leading or lagging: How well are climate change considerations being incorporated into Canadian fisheries management?","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Oceans Limited (Canada); Dalhousie University","funders":"Canada First Research Excellence Fund; Ocean Frontier Institute","keywords":"Fisheries management; Climate change; Lagging; Fisheries science; Stock assessment; Environmental resource management; Fishery; Stock (firearms); Ecosystem; Ecosystem-based management; Uncertainty; Geography; Environmental science; Fishing; Ecology; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.0252086,0.0005653764,0.0006457517,0.01239488,0.01414146,0.0151648,0.00219303,0.001684544,0.00220954],"category_scores_gemma":[0.08056699,0.0003786484,0.0008306718,0.02397522,0.006901743,0.009723837,0.002946188,0.002322382,0.0002065708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.07447021,"about_ca_system_score_gemma":0.1468115,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.960766,"about_ca_topic_score_gemma":0.9816564,"domain_scores_codex":[0.9800522,0.003817293,0.001403622,0.000941516,0.01141842,0.002367009],"domain_scores_gemma":[0.8921978,0.02278707,0.009276238,0.002926332,0.0678326,0.004979899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002434679,0.0001173913,0.3136812,0.003842755,0.0003888465,0.001098381,0.1100313,0.002241994,0.005764327,0.03221475,0.02779918,0.5025764],"study_design_scores_gemma":[0.00002635748,0.0001190095,0.5293914,0.005043031,0.0006628673,0.0003068936,0.1306036,0.001406883,0.002511477,0.006952449,0.3226289,0.0003471712],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5700927,0.05739756,0.00525462,0.1691337,0.001911147,0.000347259,0.003604729,0.000260953,0.1919972],"genre_scores_gemma":[0.9508357,0.02833479,0.007728413,0.006577169,0.0002971476,0.00005469033,0.000836944,0.00006469489,0.005270403],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07447021,"threshold_uncertainty_score":0.5403218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0484729818592486,"score_gpt":0.2470977249343362,"score_spread":0.1986247430750875,"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."}}