{"id":"W4416098230","doi":"10.1093/bioadv/vbaf287","title":"ntRoot: computational inference of human ancestry at scale from genomic data","year":2024,"lang":"en","type":"article","venue":"Bioinformatics Advances","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Canada's Michael Smith Genome Sciences Centre","funders":"Canadian Institutes of Health Research","keywords":"Inference; Scale (ratio); Genomics; Big data; Genetic data; Computational model","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00389218,0.001270411,0.001532765,0.001973413,0.001054351,0.002489845,0.002796586,0.00125177,0.01611913],"category_scores_gemma":[0.02649202,0.001070075,0.001821867,0.002267493,0.001006447,0.002112293,0.003208457,0.001981931,0.006823548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009336567,"about_ca_system_score_gemma":0.001947847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007481275,"about_ca_topic_score_gemma":0.01558463,"domain_scores_codex":[0.9983276,0.0005693478,0.00009580488,0.0005353505,0.0004017382,0.00007016458],"domain_scores_gemma":[0.9938487,0.004246867,0.00031865,0.001005681,0.0003642534,0.0002158834],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002201695,0.0002424762,0.03812981,0.002556392,0.002157713,0.001477766,0.001520816,0.3783853,0.01739988,0.05403427,0.1909368,0.3109571],"study_design_scores_gemma":[0.0002935062,0.00005884377,0.002726693,0.000150402,0.0001529841,0.0003607238,0.0001382218,0.899912,0.003911083,0.06640628,0.02581201,0.00007719776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02825265,0.0008645536,0.8795162,0.001095383,0.0003300411,0.0002004093,0.03098816,0.05424539,0.004507287],"genre_scores_gemma":[0.1473089,0.0006544394,0.7852114,0.0007804244,0.0002492692,0.0006374892,0.0508207,0.009894628,0.004442687],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01611913,"threshold_uncertainty_score":0.05392385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03687343643295704,"score_gpt":0.3371535968671262,"score_spread":0.3002801604341692,"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."}}