{"id":"W2920546078","doi":"10.1016/j.fsigen.2019.03.003","title":"Applicability of the SNPforID 52-plex panel for human identification and ancestry evaluation in a Brazilian population sample by next-generation sequencing","year":2019,"lang":"en","type":"article","venue":"Forensic Science International Genetics","topic":"Forensic and Genetic Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"National Institute of Justice; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Agilent Technologies","keywords":"Single-nucleotide polymorphism; SNP; Population; Forensic genetics; Forensic science; Genetics; Biology; Sample (material); Identification (biology); DNA sequencing; Forensic identification; Allele; Allele frequency; Computational biology; DNA profiling; SNP array; Microsatellite; Genotype; Gene; DNA; Medicine","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":[0.00311139,0.0005825068,0.0005363837,0.001960783,0.0009495061,0.001019465,0.0005545037,0.0006771268,0.002283131],"category_scores_gemma":[0.005101036,0.0003279737,0.0004313561,0.001267724,0.0004360242,0.0002438001,0.0008733164,0.0003850945,0.0005666853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000673627,"about_ca_system_score_gemma":0.001132806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02854683,"about_ca_topic_score_gemma":0.04750501,"domain_scores_codex":[0.998659,0.0004910614,0.00008947729,0.00039654,0.000237106,0.0001266562],"domain_scores_gemma":[0.9991201,0.0002531987,0.00008940086,0.0001714187,0.0003157043,0.0000501414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006377972,0.0001832887,0.7208602,0.0003612704,0.0006280604,0.00197362,0.005489422,0.002528848,0.06217486,0.003375814,0.004270991,0.1975159],"study_design_scores_gemma":[0.0001019845,0.0003600716,0.893369,0.000629488,0.001167861,0.007891621,0.002295782,0.008488938,0.01747369,0.004590429,0.06356085,0.00007033907],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9523122,0.003009395,0.02450262,0.0009673637,0.00009340236,0.0002051466,0.004994988,0.0001670161,0.01374777],"genre_scores_gemma":[0.9768399,0.00110486,0.01683861,0.0003167307,0.000029032,0.0001679252,0.00194602,0.00004746954,0.002709367],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02854683,"threshold_uncertainty_score":0.05676138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1187609405704691,"score_gpt":0.3726197132994396,"score_spread":0.2538587727289705,"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."}}