{"id":"W2793949339","doi":"10.1111/eva.12622","title":"Disentangling genetic structure for genetic monitoring of complex populations","year":2018,"lang":"en","type":"article","venue":"Evolutionary Applications","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trent University; Université Laval","funders":"National Institute for Mathematical and Biological Synthesis; National Aeronautics and Space Administration; University of Tennessee; National Science Foundation","keywords":"Population; Inference; Coalescent theory; Population size; Biology; Population genetics; Genetic structure; Range (aeronautics); Evolutionary biology; Effective population size; Ecology; Econometrics; Computer science; Artificial intelligence; Genetics; Genetic variation; Mathematics; Phylogenetics","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":[0.009079784,0.0004654659,0.0006479325,0.002096339,0.0006018698,0.001348603,0.001262086,0.0007268459,0.00170231],"category_scores_gemma":[0.03501621,0.0003397744,0.0008583897,0.001633164,0.0009596637,0.002780449,0.001555507,0.001615134,0.000219304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007487308,"about_ca_system_score_gemma":0.0008519369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006125535,"about_ca_topic_score_gemma":0.00916055,"domain_scores_codex":[0.9975647,0.001527937,0.0001177119,0.0004853354,0.0002422368,0.00006204282],"domain_scores_gemma":[0.9664164,0.02383354,0.004451537,0.003002968,0.001254297,0.001041348],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003508731,0.0002600491,0.595897,0.0003480615,0.0008248498,0.0002912882,0.0009179147,0.1586236,0.01565911,0.02568454,0.001678857,0.1994638],"study_design_scores_gemma":[0.00002806281,0.0001560237,0.06679852,0.0000611077,0.0001018337,0.0001610926,0.0001549522,0.8880984,0.002922633,0.0401556,0.001306254,0.00005556347],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4279861,0.0004160198,0.5683473,0.0007472127,0.00003002552,0.00006962712,0.0003449846,0.0005076277,0.001551157],"genre_scores_gemma":[0.900029,0.0001226492,0.09909863,0.0001084617,0.00002141014,0.00004837432,0.0002336468,0.00005759415,0.0002801621],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009079784,"threshold_uncertainty_score":0.04801905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0303329974841003,"score_gpt":0.2919428426149695,"score_spread":0.2616098451308692,"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."}}