{"id":"W2736294284","doi":"10.1002/gepi.22059","title":"Adaptive testing for association between two random vectors in moderate to high dimensions","year":2017,"lang":"en","type":"article","venue":"Genetic Epidemiology","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of General Medical Sciences; National Institute on Aging; DoD Alzheimer's Disease Neuroimaging Initiative; Canadian Institutes of Health Research; National Institutes of Health; Alzheimer's Disease Neuroimaging Initiative; National Heart, Lung, and Blood Institute; University of Minnesota; U.S. Department of Defense","keywords":"Kernel (algebra); Computer science; Computerized adaptive testing; High dimensional; Multivariate statistics; Mathematics; Algorithm; Data mining; Machine learning; Statistics; Artificial intelligence","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":[0.04467023,0.001272077,0.002919493,0.002589087,0.001362673,0.002169623,0.004330663,0.003126187,0.006388266],"category_scores_gemma":[0.1967439,0.0005811426,0.003098786,0.003335992,0.006044003,0.003798165,0.003989412,0.005281567,0.000768532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008050918,"about_ca_system_score_gemma":0.001765327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001066214,"about_ca_topic_score_gemma":0.0008262167,"domain_scores_codex":[0.936091,0.04359784,0.00287914,0.01240487,0.003760653,0.001266592],"domain_scores_gemma":[0.6450083,0.3158991,0.01164583,0.02202411,0.00370876,0.001713927],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002778081,0.0008584656,0.3143674,0.00105227,0.004624029,0.002687756,0.001109756,0.08999828,0.009398475,0.2466361,0.006025351,0.320464],"study_design_scores_gemma":[0.0005787028,0.001805977,0.06990061,0.0002316948,0.0005713226,0.001752164,0.000527953,0.5516092,0.005751488,0.3617809,0.005280394,0.000209617],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1233341,0.0004740685,0.8718895,0.0007377838,0.0001699452,0.0002789687,0.0006237947,0.0006165729,0.00187507],"genre_scores_gemma":[0.7873205,0.0002175722,0.2082689,0.0005610384,0.0002628266,0.001019263,0.00118243,0.0001352273,0.001032301],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04467023,"threshold_uncertainty_score":0.2362415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3294045891769443,"score_gpt":0.4652894558410517,"score_spread":0.1358848666641074,"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."}}