{"id":"W6977152888","doi":"10.6084/m9.figshare.29087296","title":"Additional file 7 of Dynamic clustering of genomics cohorts beyond race, ethnicity—and ancestry","year":2025,"lang":"en","type":"article","venue":"Figshare","topic":"Insect-Plant Interactions and Control","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":[0.002486675,0.001453691,0.001568,0.002671385,0.001305763,0.00291053,0.002112462,0.001498538,0.7657254],"category_scores_gemma":[0.03562562,0.0007469335,0.00179366,0.003917746,0.0003706648,0.001645045,0.001845402,0.001202438,0.1667223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008991808,"about_ca_system_score_gemma":0.001972128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01228411,"about_ca_topic_score_gemma":0.02247385,"domain_scores_codex":[0.9986993,0.0002904531,0.0001518501,0.0004801133,0.0001773267,0.0002010659],"domain_scores_gemma":[0.9761235,0.01876862,0.0008750112,0.001935502,0.001477053,0.000820298],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002530537,0.00004320054,0.007216386,0.001265012,0.0001867557,0.00009508864,0.0001203216,0.0005755383,0.0001986416,0.0008568996,0.9820755,0.007113713],"study_design_scores_gemma":[0.003009262,0.0002345524,0.06365641,0.002412501,0.0006666483,0.0009058531,0.0007393216,0.004292576,0.001289088,0.02197597,0.9005494,0.0002684403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.0003106873,0.00002594985,0.0006485487,0.00009014955,0.00004143868,0.00002977998,0.9974347,0.0008053307,0.0006134436],"genre_scores_gemma":[0.01295003,0.0001341096,0.007039881,0.0006132814,0.0001429002,0.0008817993,0.9676871,0.002710033,0.007840713],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7657254,"threshold_uncertainty_score":0.3341642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01589910094804772,"score_gpt":0.2281530536919225,"score_spread":0.2122539527438748,"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."}}