{"id":"W2765511909","doi":"10.1111/eva.12566","title":"Predicting the genetic impact of stocking in Brook Charr (<i>Salvelinus fontinalis</i>) by combining <scp>RAD</scp> sequencing and modeling of explanatory variables","year":2017,"lang":"en","type":"article","venue":"Evolutionary Applications","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Stocking; Biology; Salvelinus; Population; Genetic variation; Ecology; Population genetics; Overfishing; Population size; Fishery; Fishing; Trout; Demography; Genetics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001676993,0.0001022446,0.0001249475,0.00004117817,0.0003620569,0.00001720844,0.0002710789,0.0000987552,0.000002762329],"category_scores_gemma":[0.00008192724,0.00009501896,0.00005816033,0.00005289162,0.0001247958,0.00001311662,0.000147391,0.00009356663,3.404361e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002521366,"about_ca_system_score_gemma":0.0001066058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005378555,"about_ca_topic_score_gemma":0.00002174314,"domain_scores_codex":[0.9992459,0.00004058682,0.0002472155,0.0002134214,0.0001167138,0.0001361373],"domain_scores_gemma":[0.9991804,0.00003831687,0.0002617959,0.0003849484,0.00009342751,0.00004106364],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001742223,0.00005271182,0.6496952,0.00007158818,0.0001246277,5.639087e-7,0.0007062372,0.1324569,0.2141998,0.001164952,0.0007123196,0.0007977684],"study_design_scores_gemma":[0.001451428,0.0001993629,0.7897243,0.0001446611,0.0001392813,0.0000662331,0.002133896,0.1872352,0.01143192,0.005927182,0.001282864,0.0002636156],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9768222,0.001802756,0.02049871,0.00002387029,0.00002527847,0.0002582666,0.0001463662,0.000005676335,0.0004168971],"genre_scores_gemma":[0.9978845,0.00007701349,0.001785243,0.00001359851,0.00005346968,0.00003255549,0.0000951836,0.000008830966,0.00004966974],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2027678,"threshold_uncertainty_score":0.3874761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01655338796916013,"score_gpt":0.2563288843503299,"score_spread":0.2397754963811697,"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."}}