{"id":"W1542819714","doi":"10.1002/gepi.21911","title":"Approximate score‐based testing with application to multivariate trait association analysis","year":2015,"lang":"en","type":"article","venue":"Genetic Epidemiology","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; National Institute on Drug Abuse; Canadian Institutes of Health Research; National Institute on Aging; National Institutes of Health; Northern California Institute for Research and Education; National Heart, Lung, and Blood Institute; U.S. Department of Defense","keywords":"Score; Score test; Multivariate statistics; Statistics; Mathematics; Unavailability; Support vector machine; Covariance matrix; Kernel (algebra); Kernel method; Quantitative trait locus; Computer science; Statistical hypothesis testing; Artificial intelligence; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002974408,0.0002843158,0.0006826735,0.0002052146,0.0001161425,0.00001082523,0.0002958849,0.0004109287,0.00000912037],"category_scores_gemma":[0.006731624,0.0002519502,0.0001514434,0.000847332,0.00006191002,0.000003119744,0.00009704096,0.0001450638,0.00004251256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001480585,"about_ca_system_score_gemma":0.0002126812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005652633,"about_ca_topic_score_gemma":0.0005578828,"domain_scores_codex":[0.9965954,0.0009340652,0.0007303553,0.0008521083,0.000160491,0.0007275456],"domain_scores_gemma":[0.9973783,0.0005490868,0.0005973754,0.0006291294,0.000480329,0.0003657787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006588051,0.00004964028,0.7820687,0.00000543081,0.0003471758,7.417753e-7,0.00003340529,0.2064229,0.005619764,0.00004110571,0.00135491,0.003990299],"study_design_scores_gemma":[0.000959724,0.000761841,0.9182947,0.000006236143,0.0004514595,0.000006957252,0.00004855271,0.07208959,0.0005381094,0.0009257843,0.005501282,0.0004158118],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4335845,0.0001114843,0.5638431,0.001435061,0.00007463592,0.0003860259,0.00004008434,0.00003620084,0.0004889402],"genre_scores_gemma":[0.7323964,0.00000516079,0.2641866,0.002322989,0.0002202469,0.0002560562,0.0003862867,0.0000286108,0.000197696],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2996565,"threshold_uncertainty_score":0.9999933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05350507616428657,"score_gpt":0.3122939737650955,"score_spread":0.258788897600809,"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."}}