{"id":"W6977826031","doi":"10.6084/m9.figshare.29087293","title":"Additional file 6 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":[0.002604336,0.00156296,0.001692311,0.003099909,0.001436401,0.003303484,0.002304497,0.001517222,0.7912143],"category_scores_gemma":[0.03884383,0.0009021621,0.001761038,0.00443192,0.0004099966,0.001780409,0.001893614,0.001326101,0.1818424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009853918,"about_ca_system_score_gemma":0.002185257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01226515,"about_ca_topic_score_gemma":0.02224733,"domain_scores_codex":[0.9985561,0.0003119078,0.0001783986,0.0005389058,0.0002139061,0.0002007715],"domain_scores_gemma":[0.9716954,0.0221413,0.001022461,0.002369852,0.001819158,0.0009518088],"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":[0.0002403196,0.00004452305,0.005434965,0.001254706,0.0001611956,0.0000912723,0.0001105636,0.0006136323,0.000172365,0.0009654706,0.9841183,0.006792595],"study_design_scores_gemma":[0.003121179,0.0001875266,0.04387816,0.001954977,0.0005337325,0.000834109,0.0005291788,0.003948174,0.001146728,0.02514569,0.9184695,0.000250981],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.000254747,0.00002758337,0.0007718701,0.00009673718,0.00004953609,0.00003550711,0.9971169,0.0009808443,0.0006662096],"genre_scores_gemma":[0.01058378,0.0001485626,0.008199343,0.00064116,0.0001720701,0.0008936244,0.9687292,0.003275117,0.00735722],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.7912143,"threshold_uncertainty_score":0.2978075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01471989309365889,"score_gpt":0.2684628315744433,"score_spread":0.2537429384807844,"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."}}