{"id":"W3035353477","doi":"10.1111/eva.13035","title":"Demographic history and genomics of local adaptation in blue tit populations","year":2020,"lang":"en","type":"article","venue":"Evolutionary Applications","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Biology; Local adaptation; Population genomics; Adaptation (eye); Demographic history; Evolutionary biology; Selection (genetic algorithm); Genomics; Effective population size; Population; Natural selection; Population genetics; Ecology; Genome; Genetic variation; Genetics; Demography; Gene","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":[0.0003169336,0.00008115787,0.0001469642,0.0006053047,0.000226643,0.0002173431,0.0001577372,0.0001546198,0.0007631737],"category_scores_gemma":[0.0005526833,0.00007864123,0.0001207597,0.0004065179,0.0002707532,0.0001617909,0.0002667959,0.0001977414,0.00007611358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001455257,"about_ca_system_score_gemma":0.0001157508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002662809,"about_ca_topic_score_gemma":0.004068343,"domain_scores_codex":[0.9999056,0.00002809855,0.000004183333,0.0000359607,0.00001093232,0.00001518535],"domain_scores_gemma":[0.9996675,0.0001094984,0.00008891055,0.00002272606,0.00005431946,0.00005702156],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002988204,0.00007717829,0.8071588,0.0000595447,0.0001687295,0.0002711977,0.001848727,0.002129214,0.1724066,0.0006238651,0.0001594786,0.01479802],"study_design_scores_gemma":[0.00000271607,0.00004727765,0.9982771,0.000002779663,0.000008101221,0.00008714735,0.000185395,0.0008350638,0.0003246327,0.0001029076,0.0001233672,0.000003466775],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9996452,0.00003332936,0.0001527114,0.000007581476,4.089667e-7,8.401566e-7,0.00003147758,0.00000212265,0.0001263658],"genre_scores_gemma":[0.9996846,0.00001705149,0.0001048896,0.00000749045,0.000001414153,0.000002806196,0.00008808143,0.000001759908,0.00009199377],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002662809,"threshold_uncertainty_score":0.005294681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02253817070803526,"score_gpt":0.2157932399646416,"score_spread":0.1932550692566063,"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."}}