{"id":"W2946273399","doi":"10.3389/fgene.2019.00498","title":"Whole Genome Linkage Disequilibrium and Effective Population Size in a Coho Salmon (Oncorhynchus kisutch) Breeding Population Using a High-Density SNP Array","year":2019,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of Victoria","funders":"Fondo de Fomento al Desarrollo Científico y Tecnológico; Comisión Nacional de Investigación Científica y Tecnológica; Genome Canada; Universidad de Chile; Ministerio de Economía, Fomento y Turismo, Chile; Genome British Columbia; Government of Canada","keywords":"Linkage disequilibrium; Oncorhynchus; Biology; SNP; Tag SNP; Population; Genetics; Disequilibrium; SNP array; Evolutionary biology; Single-nucleotide polymorphism; Fishery; Genotype; Gene; Fish <Actinopterygii>; Demography; Medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002221249,0.0002589647,0.0003425903,0.0001151803,0.00005469928,0.00003651786,0.0001492402,0.0003164801,0.000004963352],"category_scores_gemma":[0.00006185602,0.0002917692,0.00005674043,0.0001807491,0.00006093492,0.00001267753,0.0001115557,0.0001891495,0.000002203102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001094511,"about_ca_system_score_gemma":0.00003064516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003128615,"about_ca_topic_score_gemma":0.0001080442,"domain_scores_codex":[0.9983823,0.0001392617,0.0003611643,0.0005838104,0.0001722701,0.0003612407],"domain_scores_gemma":[0.9993662,0.00002632222,0.000144,0.0003317124,0.00003915061,0.00009257947],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001180542,0.00004153859,0.728628,0.00005501972,0.00002356757,0.00000119403,0.0001958147,0.0109678,0.2580615,0.00002501111,0.00001901297,0.001863443],"study_design_scores_gemma":[0.001109011,0.0003373774,0.9907088,0.0000526241,0.00003162102,0.000007183329,0.0001069657,0.001384928,0.003626229,0.002180487,0.0001142928,0.0003405057],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9863008,0.001239243,0.0107526,0.00002559076,0.0007306526,0.0008232773,0.00003067448,0.00001309029,0.00008411364],"genre_scores_gemma":[0.9428657,0.00005059362,0.05644773,0.00005414269,0.000230402,0.00001343767,0.0001957311,0.00004091031,0.0001013891],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2620807,"threshold_uncertainty_score":0.9999534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00652416247997295,"score_gpt":0.2243056544144173,"score_spread":0.2177814919344443,"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."}}