{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01324988,0.001162962,0.001923804,0.002381039,0.0005288851,0.001422913,0.002909482,0.001569883,0.002683935],"category_scores_gemma":[0.08601455,0.0006326109,0.001408369,0.002860499,0.002274489,0.001906679,0.003127792,0.002094512,0.0007784854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007582472,"about_ca_system_score_gemma":0.001615532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001892523,"about_ca_topic_score_gemma":0.001471892,"domain_scores_codex":[0.9835144,0.01229919,0.0005633138,0.001042432,0.002307547,0.0002730223],"domain_scores_gemma":[0.9562242,0.03675278,0.001536544,0.002983035,0.002099653,0.0004037936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005112191,0.0001512351,0.01276594,0.0003108112,0.0004375505,0.000407304,0.0002639738,0.411795,0.00431576,0.1896613,0.002198226,0.3771815],"study_design_scores_gemma":[0.00004520362,0.00009477096,0.001299718,0.00001876528,0.00002735712,0.0001375168,0.0000195622,0.9073371,0.0008311403,0.08892049,0.001241014,0.00002730945],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002751935,0.00005727821,0.9967802,0.00004631648,0.000009967832,0.0000204844,0.00002325548,0.000158525,0.0001521082],"genre_scores_gemma":[0.1851943,0.0002153907,0.8121637,0.0001733896,0.00009287572,0.0005362621,0.0003846608,0.0001938133,0.0010456],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01324988,"threshold_uncertainty_score":0.07007295,"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."}}