{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002706945,0.0002436051,0.0002397882,0.00003477595,0.0004118168,0.00002787503,0.0006113225,0.0001067988,0.003120694],"category_scores_gemma":[0.00001470583,0.000195915,0.00008236065,0.0003107823,0.0003155554,0.00007465167,0.0007935528,0.0001201453,0.002514432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002586478,"about_ca_system_score_gemma":0.00001917231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006482209,"about_ca_topic_score_gemma":0.005044974,"domain_scores_codex":[0.9983088,0.0001037222,0.0003165405,0.0004828318,0.0003243395,0.000463744],"domain_scores_gemma":[0.9989423,0.0003027367,0.0001440242,0.0005218204,0.00001227067,0.00007684213],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000008360108,0.00002560062,0.8609152,0.000006333773,0.00008004213,0.000009631499,0.00101216,0.003871458,0.0008331938,0.00003782133,0.05702326,0.07617688],"study_design_scores_gemma":[0.0001091353,0.00002804163,0.5718491,0.000001395326,0.00003773483,4.9494e-7,0.0001738695,0.0001416327,0.00001932988,0.0008731189,0.426608,0.0001581782],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8826721,0.08848809,0.00008087551,0.002384933,0.00153488,0.0007495145,0.00006781446,0.000335139,0.0236867],"genre_scores_gemma":[0.8840411,0.1017373,0.001002503,0.003981533,0.0003861795,0.000133868,0.0001800198,0.00004480244,0.008492701],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3695847,"threshold_uncertainty_score":0.9982622,"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."}}