{"id":"W2552674072","doi":"10.1111/eva.12450","title":"A genome scan for selection signatures comparing farmed Atlantic salmon with two wild populations: Testing colocalization among outlier markers, candidate genes, and quantitative trait loci for production traits","year":2016,"lang":"en","type":"article","venue":"Evolutionary Applications","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada; Cooke Aquaculture (Canada); University of Guelph","funders":"Fisheries and Oceans Canada; Natural Sciences and Engineering Research Council of Canada; Genome Canada","keywords":"Biology; Quantitative trait locus; Genetics; Candidate gene; Population; Genome Scan; Domestication; Locus (genetics); Evolutionary biology; Genome; Selection (genetic algorithm); Gene; Allele; Microsatellite","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007876265,0.0002194908,0.0004756192,0.001232559,0.0005239739,0.0004501744,0.0003506354,0.0002990413,0.002028587],"category_scores_gemma":[0.001316063,0.0001684788,0.0005573736,0.00147929,0.0002576227,0.0001855871,0.0005207133,0.0003102447,0.0001907384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002847087,"about_ca_system_score_gemma":0.0004598478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007786915,"about_ca_topic_score_gemma":0.02821442,"domain_scores_codex":[0.9994316,0.00009525342,0.00003759133,0.000293027,0.00008150387,0.00006098527],"domain_scores_gemma":[0.9990612,0.0004290362,0.0002052401,0.0001011271,0.00009894407,0.0001044827],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008656198,0.0001234664,0.6767005,0.0001739096,0.001023309,0.0005447917,0.000670373,0.001378641,0.2805034,0.0005292523,0.001302189,0.03618456],"study_design_scores_gemma":[0.00004827407,0.0001322313,0.9888897,0.000007793038,0.0001492882,0.0002365193,0.0001955536,0.00375045,0.004813177,0.0001876987,0.001574548,0.00001478203],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9917114,0.000052343,0.00344044,0.00002609903,0.000002871755,0.00001463121,0.004229032,0.0000938151,0.0004293968],"genre_scores_gemma":[0.9743091,0.00003418812,0.01324921,0.00005187619,0.000004253289,0.00006026962,0.01179624,0.00006291115,0.0004318111],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007786915,"threshold_uncertainty_score":0.0154832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02009631510605991,"score_gpt":0.2614688437997276,"score_spread":0.2413725286936677,"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."}}