{"id":"W4319827636","doi":"10.1093/g3journal/jkad033","title":"Population-size history inferences from the coho salmon ( <i>Oncorhynchus kisutch</i> ) genome","year":2023,"lang":"en","type":"article","venue":"G3 Genes Genomes Genetics","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; Université Laval; Simon Fraser University; University of Victoria; Fisheries and Oceans Canada","funders":"National Oceanic and Atmospheric Administration; Compute Canada; Canada's Michael Smith Genome Sciences Centre; Genome Canada; McGill University; Simon Fraser University; Washington State University; Natural Sciences and Engineering Research Council of Canada; Massachusetts Department of Fish and Game","keywords":"Oncorhynchus; Spawn (biology); Biology; Population bottleneck; Demographic history; Effective population size; Fishery; Population; Gene flow; Ecology; Genetic variation; Demography; Microsatellite; Fish <Actinopterygii>; Genetics","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":[0.0003576304,0.0001721121,0.0001212385,0.0006440557,0.0003740271,0.0002465594,0.0001844413,0.0001863764,0.0009609144],"category_scores_gemma":[0.001275381,0.0001221096,0.0003365504,0.0005631378,0.0001591102,0.0002637181,0.0002282186,0.0003368431,0.0001942274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004374812,"about_ca_system_score_gemma":0.0003322541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03128141,"about_ca_topic_score_gemma":0.08499625,"domain_scores_codex":[0.9999313,0.00001610763,0.00000429993,0.00003030402,0.000009419026,0.000008588087],"domain_scores_gemma":[0.9997206,0.0001347574,0.00005739269,0.00002118305,0.00004011576,0.00002577366],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001563764,0.00008875615,0.8718502,0.0001167056,0.0003845193,0.0001943292,0.000923547,0.0196835,0.06939229,0.001929837,0.002636658,0.03264327],"study_design_scores_gemma":[0.00001437132,0.00002991932,0.9650029,0.00002269414,0.0001156916,0.00005943803,0.0003435044,0.02811939,0.001936879,0.001272637,0.003064374,0.00001816776],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9936281,0.0001318732,0.003283995,0.00007723651,0.000004932482,0.0000067035,0.001800447,0.00005551033,0.001011043],"genre_scores_gemma":[0.9910913,0.0001143947,0.003804068,0.00008401211,0.000005528589,0.00001262815,0.004655515,0.00002665433,0.0002058708],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03128141,"threshold_uncertainty_score":0.06219864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01880229741954599,"score_gpt":0.2166951025573364,"score_spread":0.1978928051377904,"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."}}