{"id":"W3152208272","doi":"10.3390/genes12040524","title":"Associative Overdominance and Negative Epistasis Shape Genome-Wide Ancestry Landscape in Supplemented Fish Populations","year":2021,"lang":"en","type":"article","venue":"Genes","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère des Forêts, de la Faune et des Parcs","keywords":"Overdominance; Epistasis; Biology; Fish <Actinopterygii>; Genome; Evolutionary biology; Genetics; Gene; Allele; Fishery","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":[0.00005710949,0.0001024003,0.0001227407,0.00002850031,0.0000945321,0.00002554547,0.00006338803,0.0001000322,0.0001800918],"category_scores_gemma":[0.00009205251,0.0001141425,0.00003757322,0.0001335192,0.00003326801,0.000005959686,0.0001008162,0.00005400386,0.000002159353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001173943,"about_ca_system_score_gemma":0.00004810797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003309158,"about_ca_topic_score_gemma":0.0011265,"domain_scores_codex":[0.9992883,0.00006321546,0.0001375887,0.0002689798,0.00008596875,0.0001559586],"domain_scores_gemma":[0.9996563,0.00001649252,0.0000718361,0.0001290781,0.00008382905,0.00004243032],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003079996,0.00002310151,0.9840738,0.00001276018,0.0000395553,0.00001005942,0.000293024,0.0002442318,0.01152136,0.00006553686,0.001623234,0.002062587],"study_design_scores_gemma":[0.0006448018,0.00002866837,0.9670967,0.000006630647,0.0000151942,0.000004671381,0.0006741668,0.00006698607,0.01466234,0.0003267761,0.01632799,0.0001450125],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997369,0.001008891,0.0001143703,0.0005519758,0.00009061035,0.00008848395,0.0003433377,0.000004627451,0.0004287295],"genre_scores_gemma":[0.9955493,0.0004386699,0.002021962,0.0004848755,0.00007122435,0.000006820432,0.0007945743,0.000007002147,0.0006255909],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01697699,"threshold_uncertainty_score":0.4654595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02364124491053616,"score_gpt":0.2662971468243071,"score_spread":0.242655901913771,"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."}}