{"id":"W4406864540","doi":"10.1016/j.fsigen.2025.103232","title":"X-chromosomal STRs: Metapopulations and mutation rates","year":2025,"lang":"en","type":"article","venue":"Forensic Science International Genetics","topic":"Forensic and Genetic Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cytodiagnostics (Canada)","funders":"European Regional Development Fund; Programa Operacional Temático Factores de Competitividade; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; Ministério da Ciência, Tecnologia e Inovação; Fundação para a Ciência e a Tecnologia; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Metapopulation; Biology; Mutation; Mutation rate; Genetics; Evolutionary biology; Gene; Demography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002434964,0.00009603429,0.0000710617,0.0001675682,0.000171397,0.0001100848,0.0003071934,0.00005433361,0.00002395501],"category_scores_gemma":[0.0002482334,0.00009011308,0.00003325941,0.0002790694,0.000759388,0.000009579021,0.0002346575,0.0000545194,0.000007149592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002260267,"about_ca_system_score_gemma":0.0001977966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001961599,"about_ca_topic_score_gemma":0.000047068,"domain_scores_codex":[0.9989114,0.00001679879,0.0001811065,0.000355107,0.0003339117,0.0002017165],"domain_scores_gemma":[0.9992831,0.00001662624,0.00004322334,0.0002152988,0.0003742237,0.00006749532],"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":[0.0001148448,0.0001200172,0.09580471,0.00002532241,0.0002140393,0.00001025437,0.0002688824,0.003694976,0.6557654,0.04277721,0.008258746,0.1929457],"study_design_scores_gemma":[0.0008644763,0.0002943909,0.2487567,0.00003538122,0.00003328784,0.00005742809,0.0004391508,0.0250083,0.6860638,0.0133156,0.02480619,0.0003252531],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9872677,0.0005854018,0.004767974,0.0007895109,0.0005985733,0.0001366468,0.00001479236,0.000008294092,0.005831124],"genre_scores_gemma":[0.9895121,0.0001396287,0.008151064,0.0002337533,0.0001148409,0.0000122713,0.00006702085,0.000005802957,0.00176354],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1926204,"threshold_uncertainty_score":0.3674704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01494044182359816,"score_gpt":0.3492088303191046,"score_spread":0.3342683884955064,"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."}}