{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001317857,0.0001736639,0.0001384535,0.00006898912,0.0004647267,0.00001833272,0.0000900842,0.00009715905,0.000002860202],"category_scores_gemma":[0.00007949895,0.0001461017,0.00003723567,0.0001864056,0.0002014651,0.0000215155,0.00002041332,0.00004624331,5.542123e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005565874,"about_ca_system_score_gemma":0.0001046591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005468134,"about_ca_topic_score_gemma":0.0005012639,"domain_scores_codex":[0.9988863,0.00004498549,0.0002403348,0.0005034683,0.00009632675,0.0002285802],"domain_scores_gemma":[0.9992885,0.00006306164,0.0001577007,0.000136236,0.0002828413,0.00007166735],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001405754,0.0003536398,0.2102311,0.0002261892,0.0003367812,8.635352e-8,0.0002491761,0.0612038,0.6688733,0.043006,0.001626874,0.0124873],"study_design_scores_gemma":[0.001372579,0.0007307788,0.9827312,0.00004859228,0.0001468448,0.00001903539,0.0001240076,0.003597938,0.001428002,0.004345936,0.005092855,0.0003622434],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4744302,0.0004241814,0.5230476,0.0001621534,0.00002786468,0.001737331,0.0001048704,0.00003105284,0.00003472025],"genre_scores_gemma":[0.8540378,0.00001502866,0.1428469,0.00001906021,0.0002285346,0.001804973,0.0007891448,0.00002930072,0.0002292942],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7725002,"threshold_uncertainty_score":0.5957855,"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."}}