{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038344,0.0001739617,0.0001743069,0.0003159709,0.0001784875,0.0001268807,0.0007127257,0.0001048601,0.00009524979],"category_scores_gemma":[0.0002016555,0.0001655448,0.0002044046,0.0006471969,0.0004912079,0.00001543804,0.0002095041,0.00008485132,0.00003786327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008965543,"about_ca_system_score_gemma":0.0003444262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003967266,"about_ca_topic_score_gemma":0.0001128944,"domain_scores_codex":[0.9978616,0.0000198697,0.0003205584,0.0006765311,0.0006695711,0.0004518431],"domain_scores_gemma":[0.9985276,0.00002468304,0.0001378272,0.0005006825,0.0006638746,0.0001452755],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002132519,0.00008453985,0.3272609,0.00002540741,0.0005549797,0.000004351188,0.0002348739,0.112034,0.5341215,0.00500821,0.004594517,0.01586351],"study_design_scores_gemma":[0.002263567,0.0008996726,0.3124739,0.00003737559,0.0002279316,0.00005057923,0.001643199,0.4473493,0.1687652,0.004800177,0.06038116,0.001107927],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9608152,0.0001615975,0.03322885,0.0004395539,0.0008770942,0.0004854767,0.0001199385,0.00001439038,0.003857903],"genre_scores_gemma":[0.9310926,0.00003989153,0.06374528,0.0001032228,0.0002460933,0.00004397125,0.0003592508,0.00001663077,0.004353063],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3653564,"threshold_uncertainty_score":0.6750723,"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."}}