{"id":"W4413745654","doi":"10.1111/eva.70149","title":"Application of Genomic Offsets to Inform Freshwater Fisheries Management Under Climate Change","year":2025,"lang":"en","type":"article","venue":"Evolutionary Applications","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Yukon Health and Social Services; Yukon University; Yukon Department of Environment; Ministry of Forests; Government of British Columbia; University of British Columbia; Freshwater Fisheries Society of BC; Ministry of Environment; Fisheries and Oceans Canada; Okanagan University College; University of British Columbia, Okanagan Campus","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Genome British Columbia; Parks Canada; Freshwater Fisheries Society of British Columbia","keywords":"Biology; Climate change; Fisheries management; Fishery; Fisheries science; Environmental resource management; Ecology; Fishing; Environmental science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.001146072,0.0005410631,0.0003070552,0.001084706,0.0004920318,0.001110994,0.0005197141,0.0003617996,0.001231406],"category_scores_gemma":[0.002266996,0.0001316451,0.0002253899,0.001256409,0.0002718307,0.0003451548,0.0005091587,0.0005758368,0.0003211107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001300729,"about_ca_system_score_gemma":0.001852759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09256844,"about_ca_topic_score_gemma":0.3032387,"domain_scores_codex":[0.9995872,0.00008765875,0.0000214765,0.0001459591,0.0001054029,0.0000522867],"domain_scores_gemma":[0.9991499,0.0001939732,0.0002549076,0.00007574978,0.000245138,0.0000803197],"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":[0.0001744655,0.00004772124,0.8691096,0.0001346663,0.0003233262,0.0001551579,0.0003090006,0.01679713,0.01974808,0.001021006,0.001765846,0.09041414],"study_design_scores_gemma":[0.00001127257,0.0000832657,0.953136,0.00006248242,0.0001510335,0.00009166541,0.0006238989,0.02983186,0.004914045,0.003292538,0.007752775,0.00004921356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9387013,0.001748363,0.03865111,0.0007816641,0.00008021338,0.00008243516,0.01211005,0.0005626741,0.007282173],"genre_scores_gemma":[0.9746484,0.0003911113,0.02025223,0.0002061196,0.00001749242,0.0000291384,0.00355671,0.00004025841,0.0008584261],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09256844,"threshold_uncertainty_score":0.1840593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009841377322952084,"score_gpt":0.2318401076779943,"score_spread":0.2219987303550422,"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."}}