{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009172905,0.00009623052,0.00009886021,0.00008761458,0.0003599241,0.000005405488,0.0002323567,0.00004122198,0.0005354641],"category_scores_gemma":[0.000001650404,0.00009951368,0.00003177756,0.000414787,0.0001440624,0.0001441412,0.0005809434,0.00004267346,0.001177652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001487846,"about_ca_system_score_gemma":0.000002764559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006942273,"about_ca_topic_score_gemma":0.0007565428,"domain_scores_codex":[0.999238,0.00001061463,0.0002114425,0.0002464791,0.0001040641,0.0001894601],"domain_scores_gemma":[0.9995555,0.0000198903,0.00005886706,0.0003232624,0.00001217359,0.00003032998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000442784,0.0003439779,0.4114198,0.0001248504,0.0001158052,3.847409e-7,0.0002606541,0.001557075,0.000425875,0.3409579,0.2266646,0.01808481],"study_design_scores_gemma":[0.00008051939,0.00000938578,0.6629814,0.000003292235,0.00002082924,1.946279e-7,0.00008068738,0.0002545884,0.00001693268,0.007002471,0.3294872,0.00006248982],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.06963206,0.0002183608,0.2526104,0.05676698,0.0003073048,0.009853484,0.0002450031,0.0004222522,0.6099442],"genre_scores_gemma":[0.9724771,0.0002211944,0.01095532,0.004000614,0.0000325257,0.008312768,0.0001277079,0.00001016479,0.00386254],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9028451,"threshold_uncertainty_score":0.9996001,"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."}}