{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001058843,0.00008699748,0.0000844217,0.0000673869,0.0001079558,0.00006090656,0.0003200276,0.00006881633,0.0000113604],"category_scores_gemma":[0.0003390432,0.00007514655,0.00003871079,0.000195828,0.000313588,0.00002163314,0.000107901,0.00005134213,6.635559e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001048236,"about_ca_system_score_gemma":0.0001511786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000245838,"about_ca_topic_score_gemma":0.0004351862,"domain_scores_codex":[0.9985161,0.00003830083,0.0003193328,0.0004042753,0.0005569683,0.0001650226],"domain_scores_gemma":[0.9990326,0.00002455535,0.0001552548,0.0003190709,0.0004329403,0.00003561088],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001213397,0.00001494273,0.07071824,0.00001081824,0.000004550181,7.827819e-9,0.00006640323,0.001843151,0.9101421,0.0003310576,0.00008936243,0.01676719],"study_design_scores_gemma":[0.0005510125,0.0001338038,0.2468898,0.00001685847,0.00000930981,0.000002593449,0.0002073934,0.1325331,0.6142449,0.004843519,0.0004345294,0.0001330666],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961883,0.0001048053,0.002329521,0.0001453338,0.0003267283,0.0007542141,0.00006655997,0.000002312576,0.00008223603],"genre_scores_gemma":[0.9977124,0.00002350123,0.00153363,0.00004373135,0.0001034993,0.00005597121,0.0004440382,0.00000717859,0.00007607343],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2958972,"threshold_uncertainty_score":0.3064387,"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."}}