{"id":"W6939605880","doi":"10.6084/m9.figshare.29087308.v1","title":"Additional file 11 of Dynamic clustering of genomics cohorts beyond race, ethnicity—and ancestry","year":2025,"lang":"en","type":"dataset","venue":"Figshare","topic":"Mycorrhizal Fungi and Plant Interactions","field":"Agricultural and Biological 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":[0.001946774,0.001525534,0.001691163,0.002304431,0.001017722,0.002743479,0.002319571,0.001893398,0.3887236],"category_scores_gemma":[0.01754787,0.0007163283,0.001664295,0.004074851,0.0003851013,0.001184343,0.001907718,0.001493439,0.1025022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001152257,"about_ca_system_score_gemma":0.002181329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01764937,"about_ca_topic_score_gemma":0.0375971,"domain_scores_codex":[0.9989033,0.0002316425,0.0001274236,0.0004438555,0.0001271911,0.0001666402],"domain_scores_gemma":[0.9930564,0.004428728,0.0004804497,0.0008876834,0.0007152288,0.0004315616],"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":[0.0001658122,0.00003387646,0.004429859,0.001413811,0.0001569068,0.00005118916,0.00005873609,0.0004980831,0.0001409215,0.0006996039,0.9892581,0.003092977],"study_design_scores_gemma":[0.00176266,0.00009112347,0.02910796,0.001418393,0.000379035,0.0003489328,0.0002695595,0.001431338,0.0005588522,0.009023828,0.9554962,0.0001121077],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000115374,0.00003061369,0.0001206439,0.00003649126,0.00001337651,0.00001075117,0.9992932,0.0001484864,0.0002309596],"genre_scores_gemma":[0.002061966,0.00005928417,0.001001882,0.0001646742,0.00002209247,0.000241924,0.9947291,0.0002512383,0.001467749],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3887236,"threshold_uncertainty_score":0.8719116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01840752687738174,"score_gpt":0.2365623174723676,"score_spread":0.2181547905949859,"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."}}