{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00001859862,0.0001106043,0.00009614565,0.00005367828,0.000325606,0.000008717589,0.0001883769,0.0001024134,0.0000713553],"category_scores_gemma":[0.00001601762,0.0001247175,0.00007776088,0.0001508625,0.0001419663,0.000004024651,0.00005992123,0.00003570068,0.000006328631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001510969,"about_ca_system_score_gemma":0.00004358825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007487808,"about_ca_topic_score_gemma":0.00001140165,"domain_scores_codex":[0.9992225,0.00001928111,0.0002238537,0.0002673216,0.0001140253,0.00015304],"domain_scores_gemma":[0.9992255,0.000009491509,0.0001130483,0.0003380562,0.0002512205,0.00006270849],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006879427,0.00007928177,0.6900006,0.0000537974,0.0000855339,1.603497e-7,0.0001134904,0.005862411,0.2889293,0.003712976,0.004968382,0.006125243],"study_design_scores_gemma":[0.0003109287,0.00009445664,0.9322299,0.000005696381,0.00005137082,0.00001097053,0.00007800124,0.0009220819,0.007392854,0.004906285,0.0538311,0.0001663663],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8481272,0.0005112462,0.1495672,0.0001024327,0.0002186887,0.0006658097,0.0006014168,0.00002095906,0.0001851094],"genre_scores_gemma":[0.7933031,0.00001460518,0.2051424,0.00002631937,0.0007853218,0.00005674534,0.0004900739,0.00001194323,0.0001695296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2815365,"threshold_uncertainty_score":0.508583,"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."}}