{"id":"W6977289315","doi":"10.6084/m9.figshare.29087305","title":"Additional file 10 of Dynamic clustering of genomics cohorts beyond race, ethnicity—and ancestry","year":2025,"lang":"en","type":"article","venue":"Figshare","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Cluster analysis; Genomics; Population; Genome; Genetic data","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002491789,0.001559739,0.001639908,0.003075035,0.001360983,0.003205438,0.002248629,0.001484995,0.7917454],"category_scores_gemma":[0.03715265,0.0008608174,0.001600261,0.004424621,0.0003905246,0.001770274,0.001904185,0.001264949,0.1825973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009930327,"about_ca_system_score_gemma":0.002097306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01179417,"about_ca_topic_score_gemma":0.02086828,"domain_scores_codex":[0.998676,0.0002797711,0.0001591106,0.0005003005,0.0001982883,0.0001865228],"domain_scores_gemma":[0.9742939,0.02001463,0.0009562836,0.002195537,0.001681401,0.0008581372],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000226873,0.0000418073,0.004853567,0.001076875,0.0001348009,0.00008464568,0.00009436464,0.0005398311,0.0001537375,0.0008874371,0.9855314,0.006374715],"study_design_scores_gemma":[0.003188339,0.0001849871,0.04567786,0.001932195,0.0004697652,0.0007550527,0.0005374035,0.004023564,0.001129649,0.02507029,0.9167805,0.0002505238],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002498905,0.00002329583,0.0006713862,0.00009472985,0.0000470978,0.00003234654,0.9972698,0.0009636701,0.0006478027],"genre_scores_gemma":[0.01028752,0.0001293582,0.006973075,0.000585379,0.0001582242,0.0008268711,0.9708816,0.003049153,0.007108816],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7917454,"threshold_uncertainty_score":0.2970498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0157437349319587,"score_gpt":0.2717533919962629,"score_spread":0.2560096570643042,"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."}}