{"id":"W4392787369","doi":"10.1111/eva.13654","title":"Population genomics, life‐history tactics, and mixed‐stock subsistence fisheries in the northernmost American Atlantic salmon populations","year":2024,"lang":"en","type":"article","venue":"Evolutionary Applications","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère de l’Environnement, de la Lutte contre les changements climatiques, de la Faune et des Parcs; Parks Canada; Ministère des Ressources naturelles et des Forêts; Université Laval","funders":"Génome Québec; Ontario Genomics; Genome Canada","keywords":"Salmo; Fish migration; Biology; Fishery; Estuary; Ecology; Population; Stock (firearms); Tributary; Demographic history; Historical ecology; Population genomics; Life history theory; Geography; Genetic variation; Genomics; Habitat; Life history; Genome; Demography; Fish <Actinopterygii>","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":[],"consensus_categories":[],"category_scores_codex":[0.0001447302,0.0001030408,0.00009607543,0.00005253184,0.0004081614,0.0000200953,0.0001573344,0.00003159055,0.0001774158],"category_scores_gemma":[0.00002793717,0.00009203987,0.00002797503,0.0003027676,0.0003829307,0.0002240376,0.0001050516,0.0001244119,0.0001565996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003711545,"about_ca_system_score_gemma":0.00001416242,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002192265,"about_ca_topic_score_gemma":0.01987616,"domain_scores_codex":[0.9991781,0.00006090424,0.0001880469,0.0002947798,0.0001223195,0.0001559194],"domain_scores_gemma":[0.9995838,0.00010671,0.00005641755,0.0002147479,0.000005445042,0.00003288825],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000003274462,0.00005552849,0.9534025,0.00001217913,0.00001149931,0.000001295749,0.000429343,0.0003713379,0.000009222588,0.007534919,0.03725826,0.0009106427],"study_design_scores_gemma":[0.00002967774,0.0000144391,0.8356463,0.000003295403,0.00002461569,0.000004272368,0.0003349026,0.001538326,9.6154e-8,0.001566791,0.1607489,0.00008831085],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9705709,0.001004562,0.001735736,0.01422568,0.0002561934,0.001064136,0.00002650449,0.0001490274,0.01096723],"genre_scores_gemma":[0.9971802,0.0002151389,0.001007002,0.0004839245,0.00005434544,0.0004665242,0.0001105308,0.0000097108,0.000472609],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1234906,"threshold_uncertainty_score":0.9980085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01755450305095588,"score_gpt":0.2202018053611806,"score_spread":0.2026473023102247,"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."}}