{"id":"W6958702703","doi":"10.6084/m9.figshare.29087308","title":"Additional file 11 of Dynamic clustering of genomics cohorts beyond race, ethnicity—and ancestry","year":2025,"lang":"en","type":"dataset","venue":"Figshare","topic":"Scientific Computing and Data Management","field":"Decision Sciences","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":[0.002208938,0.001600032,0.001512802,0.002563549,0.001115281,0.002614316,0.002568488,0.001630354,0.5237864],"category_scores_gemma":[0.01861209,0.0007637168,0.00156202,0.004148388,0.0004489259,0.001385855,0.001729138,0.001557308,0.1141835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001465843,"about_ca_system_score_gemma":0.002790078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02107568,"about_ca_topic_score_gemma":0.04116708,"domain_scores_codex":[0.9987852,0.0002268629,0.0001481759,0.0004435604,0.000190013,0.0002061439],"domain_scores_gemma":[0.9907219,0.005937214,0.0005704984,0.00110171,0.001143312,0.0005252922],"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":[0.0001210967,0.00003340229,0.002308474,0.0008442468,0.00007342225,0.00003438443,0.00004205402,0.0003897763,0.0001027187,0.0006738504,0.9930736,0.002303],"study_design_scores_gemma":[0.002076143,0.00008624558,0.02148549,0.001159438,0.0002183602,0.0002787506,0.0002590087,0.001601015,0.0007002911,0.01043568,0.9615833,0.0001162721],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00008282143,0.00001402006,0.0001220885,0.00003424833,0.00001393726,0.00001647449,0.9992707,0.0001801415,0.0002655307],"genre_scores_gemma":[0.001803095,0.0000403116,0.001383562,0.0001631305,0.0000235111,0.0003545624,0.9941724,0.0003468549,0.00171254],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5237864,"threshold_uncertainty_score":0.679261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08988286636610304,"score_gpt":0.3591014696115767,"score_spread":0.2692186032454736,"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."}}