{"id":"W2955873362","doi":"10.1016/j.fsigen.2019.06.022","title":"MAPlex - A massively parallel sequencing ancestry analysis multiplex for Asia-Pacific populations","year":2019,"lang":"en","type":"article","venue":"Forensic Science International Genetics","topic":"Forensic and Genetic Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agencia Estatal de Investigación; European Regional Development Fund; Department of Genetics, University of Alabama at Birmingham; Alberta Foundation for the Arts; Ministerio de Agricultura, Pesca y Alimentación; Xunta de Galicia; Consellería de Economía, Emprego e Industria, Xunta de Galicia; Alzheimer's Foundation of America; Yale School of Medicine; Yale University; Harvard Medical School; Ministério da Agricultura, Pecuária e Abastecimento","keywords":"Biology; Single-nucleotide polymorphism; Massive parallel sequencing; Population; Multiplex; Evolutionary biology; Genetics; Ancestry-informative marker; Allele frequency; East Asia; Allele; Genotype; DNA sequencing; Geography; China; Gene; Archaeology; Demography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001711051,0.0009515147,0.0005930281,0.001589928,0.0007870105,0.001418068,0.0008532095,0.0006517227,0.004268536],"category_scores_gemma":[0.003470557,0.0007105892,0.0006085281,0.0009941136,0.0003918535,0.0007659693,0.001681364,0.001123101,0.001987183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003610734,"about_ca_system_score_gemma":0.0008409254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001227367,"about_ca_topic_score_gemma":0.00351398,"domain_scores_codex":[0.9984372,0.0003177493,0.00007922389,0.0005484553,0.0005167195,0.0001006197],"domain_scores_gemma":[0.9989687,0.0002711229,0.0002427659,0.0001737056,0.0002160721,0.0001275633],"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":[0.001977084,0.0003286711,0.05566046,0.0007486429,0.0005787446,0.0008194412,0.001764833,0.005241842,0.5308743,0.005845868,0.01595447,0.3802057],"study_design_scores_gemma":[0.0003777532,0.001147912,0.1120199,0.0002777883,0.0005885772,0.003472098,0.0005766919,0.08826941,0.5873519,0.008568663,0.1970817,0.0002675865],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3421634,0.001595973,0.592629,0.0005141062,0.0002713184,0.002320138,0.02554961,0.01530413,0.01965251],"genre_scores_gemma":[0.2299377,0.0007096646,0.7301283,0.0005545543,0.0001126245,0.002455402,0.01895111,0.00132189,0.01582867],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004268536,"threshold_uncertainty_score":0.01427966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06380869500976226,"score_gpt":0.3561422036390742,"score_spread":0.292333508629312,"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."}}