{"id":"W4289782615","doi":"10.1371/journal.pone.0271767","title":"Identifying signatures of natural selection in Indian populations","year":2022,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Forensic and Genetic Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Amgen (Canada); University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada; Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Mitacs; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Coalescent theory; Evolutionary biology; Biology; Natural selection; Outlier; Population; False positive paradox; Selection (genetic algorithm); Computational biology; Genetics; Computer science; Gene; Phylogenetics; Artificial intelligence; Demography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006892932,0.0002569356,0.0004866528,0.001696377,0.0006960638,0.0009420962,0.0005158652,0.0002884535,0.0006837448],"category_scores_gemma":[0.001766644,0.0001834853,0.0004713947,0.001870561,0.0008693089,0.0002188146,0.0007670036,0.0005305083,0.0001196734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002408001,"about_ca_system_score_gemma":0.0003773801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003437766,"about_ca_topic_score_gemma":0.005297277,"domain_scores_codex":[0.9993088,0.0001728369,0.000050379,0.0002310227,0.0001209609,0.0001160496],"domain_scores_gemma":[0.998676,0.0005793932,0.0002952391,0.0002012112,0.0001377823,0.0001104137],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000495301,0.00006207453,0.8621732,0.0001170085,0.0003431491,0.0007376984,0.002536041,0.001347353,0.1064872,0.0005896265,0.0001746904,0.02493656],"study_design_scores_gemma":[0.00001091234,0.00009723833,0.9913815,0.00001049113,0.0001278677,0.0007912767,0.0009513077,0.001941574,0.003420947,0.0004774331,0.0007608237,0.00002858921],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984238,0.0001012207,0.0009635438,0.00001884514,0.000002481577,0.000004578754,0.00008497756,0.00003302609,0.0003673876],"genre_scores_gemma":[0.998427,0.00006332288,0.001082028,0.00002590815,0.000005035728,0.000008737995,0.0003095557,0.00001276147,0.00006565901],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003437766,"threshold_uncertainty_score":0.00683552,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04690325572475359,"score_gpt":0.2923096491203324,"score_spread":0.2454063933955788,"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."}}