{"id":"W6958472416","doi":"10.6084/m9.figshare.29087299","title":"Additional file 8 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":[0.002539051,0.001584103,0.001648239,0.003099008,0.001350436,0.00320758,0.002310604,0.00146489,0.7913848],"category_scores_gemma":[0.03750412,0.000890061,0.001698383,0.004241132,0.000393787,0.001744286,0.001901224,0.001276712,0.1880601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009511445,"about_ca_system_score_gemma":0.002036314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01079312,"about_ca_topic_score_gemma":0.01983911,"domain_scores_codex":[0.9986337,0.0002923885,0.0001697275,0.0005061834,0.0002059418,0.0001920228],"domain_scores_gemma":[0.973763,0.02052202,0.0009444511,0.00222565,0.001693333,0.0008515578],"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":[0.0002352435,0.00004249053,0.00502974,0.001208821,0.0001499743,0.000088542,0.000103449,0.0005617425,0.0001707242,0.0008827814,0.9845468,0.006979628],"study_design_scores_gemma":[0.003089332,0.0001779233,0.04151684,0.001938586,0.0004984958,0.0007625005,0.0004935818,0.003864467,0.001160901,0.0236253,0.9226324,0.0002396008],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.0002681781,0.00002766098,0.000810532,0.0001022559,0.00005187968,0.00003702476,0.996847,0.001147704,0.0007076857],"genre_scores_gemma":[0.01069998,0.0001489196,0.008322367,0.0006435999,0.0001734469,0.0009526419,0.9674466,0.003565009,0.008047555],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.7913848,"threshold_uncertainty_score":0.2975642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01472955665848986,"score_gpt":0.2685096924877023,"score_spread":0.2537801358292125,"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."}}