{"id":"W4386221703","doi":"10.1101/2023.08.25.554311","title":"Decreased accuracy of forensic DNA mixture analysis for groups with lower genetic diversity","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"University of California, San Francisco; Genentech; National Institutes of Health; Genentech Foundation","keywords":"False positive paradox; Forensic science; Forensic genetics; DNA; Statistics; Diversity (politics); Inclusion (mineral); DNA profiling; Variation (astronomy); Human genetic variation; Allele; Computational biology; Genetics; Evolutionary biology; Biology; Mathematics; Computer science; Psychology; Human genome; Microsatellite; Social psychology; Gene; Physics; Political science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.02610538,0.001228128,0.001428197,0.004407621,0.001038384,0.004075702,0.001683839,0.00172468,0.006112092],"category_scores_gemma":[0.07306232,0.0006041189,0.00114515,0.002441886,0.001887124,0.001563816,0.002275083,0.001854669,0.00252408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009943988,"about_ca_system_score_gemma":0.0005766848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002811058,"about_ca_topic_score_gemma":0.002390998,"domain_scores_codex":[0.9739072,0.01086805,0.001207025,0.00668118,0.006380962,0.000955587],"domain_scores_gemma":[0.9151729,0.05076467,0.0111196,0.01385611,0.00845884,0.0006279153],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002098442,0.0001269996,0.6109685,0.001782151,0.003396089,0.0007023748,0.001872601,0.03681951,0.05028744,0.01470303,0.02934636,0.2478964],"study_design_scores_gemma":[0.0001268721,0.0003102885,0.4362486,0.001026485,0.002015757,0.004453063,0.001377848,0.2237566,0.1637858,0.09538026,0.07104377,0.0004746625],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5469744,0.009626701,0.4070753,0.004774456,0.00131116,0.0001616845,0.01106474,0.007214693,0.01179688],"genre_scores_gemma":[0.9200427,0.000569754,0.07131781,0.0007991311,0.0001439355,0.00005200313,0.004113229,0.0005367448,0.002424728],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02610538,"threshold_uncertainty_score":0.1380602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01594436854513031,"score_gpt":0.2303354444686469,"score_spread":0.2143910759235166,"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."}}