{"id":"W4378611268","doi":"10.1111/1755-0998.13811","title":"Genomic and machine learning‐based screening of aquaculture‐associated introgression into at‐risk wild North American Atlantic salmon ( <i>Salmo salar</i> ) populations","year":2023,"lang":"en","type":"article","venue":"Molecular Ecology Resources","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; Bedford Institute of Oceanography; Fisheries and Oceans Canada","funders":"","keywords":"Salmo; Biology; Introgression; Domestication; Single-nucleotide polymorphism; Aquaculture; SNP; Genetic admixture; Microsatellite; Fishery; Evolutionary biology; Genetics; Population; Fish <Actinopterygii>; Gene; Genotype; Allele; Demography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002060379,0.0004008412,0.0003222733,0.0008519211,0.0002439472,0.0004802709,0.0005081742,0.0004152704,0.001581621],"category_scores_gemma":[0.002970519,0.0001696168,0.0006219344,0.0004549753,0.0002727162,0.0002461026,0.0005398739,0.0004594082,0.0008623839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002573011,"about_ca_system_score_gemma":0.0003401551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002453898,"about_ca_topic_score_gemma":0.004124266,"domain_scores_codex":[0.9992399,0.000264328,0.00004813532,0.000299566,0.00009972414,0.00004833455],"domain_scores_gemma":[0.9988035,0.0005592019,0.0001808102,0.0001409823,0.0002271897,0.00008831859],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009679647,0.0005664634,0.6483607,0.0002761777,0.001009709,0.0002474005,0.0002627446,0.07113951,0.1029699,0.0007349684,0.006783074,0.1666814],"study_design_scores_gemma":[0.00009029871,0.0004273404,0.411974,0.00004689584,0.0002230054,0.0001970917,0.000151114,0.559393,0.02364583,0.00104663,0.002733452,0.00007125587],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9670811,0.000152894,0.0263436,0.0001503099,0.00002161441,0.0000822805,0.003944249,0.0009139885,0.001309972],"genre_scores_gemma":[0.9330074,0.000056446,0.0547322,0.0001967555,0.00001916442,0.0001622846,0.01055333,0.0001268102,0.001145543],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002453898,"threshold_uncertainty_score":0.0108965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008588393200744416,"score_gpt":0.2307246581510193,"score_spread":0.2221362649502748,"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."}}