{"id":"W6902431408","doi":"10.6084/m9.figshare.29087284","title":"Additional file 3 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":[0.002494697,0.001545503,0.001636835,0.003059391,0.001465702,0.003342613,0.002289331,0.001434597,0.7957529],"category_scores_gemma":[0.0377898,0.0008737526,0.001684028,0.004446431,0.0004231381,0.00176904,0.001925106,0.001289269,0.1778101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001017561,"about_ca_system_score_gemma":0.002185914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01290282,"about_ca_topic_score_gemma":0.02317582,"domain_scores_codex":[0.9986784,0.00027498,0.0001616334,0.000502567,0.0001996116,0.0001826692],"domain_scores_gemma":[0.9724886,0.02189765,0.0009513298,0.002080607,0.001715233,0.0008666604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0002436552,0.00004271088,0.005706721,0.00128735,0.0001559236,0.0000910223,0.0001177586,0.0006497637,0.0001736177,0.001067111,0.9838575,0.006606919],"study_design_scores_gemma":[0.003137767,0.0001780182,0.04111271,0.00192152,0.0005315305,0.0008395702,0.0005454416,0.004068217,0.001138865,0.02577722,0.9205083,0.0002407008],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.00024622,0.00002381016,0.0007629166,0.00008902764,0.00004066768,0.00003496041,0.9972355,0.0009100281,0.0006569352],"genre_scores_gemma":[0.01015046,0.0001318115,0.007785257,0.0006086985,0.0001464174,0.0009199255,0.9699515,0.003271788,0.007034169],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7957529,"threshold_uncertainty_score":0.2913337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01471810711127488,"score_gpt":0.2683486013305695,"score_spread":0.2536304942192947,"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."}}