{"id":"W4393120764","doi":"10.1093/jrsssb/qkae023","title":"Interpretable discriminant analysis for functional data supported on random nonlinear domains with an application to Alzheimer’s disease","year":2024,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series B (Statistical Methodology)","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute on Aging; National Science Foundation of Sri Lanka; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Pfizer; Novartis Pharmaceuticals Corporation; Meso Scale Diagnostics; Northern California Institute for Research and Education; F. Hoffmann-La Roche; University of Southern California; Bristol-Myers Squibb; Eli Lilly and Company; Biogen; BioClinica; Canadian Institutes of Health Research; National Science Foundation","keywords":"Linear discriminant analysis; Disease; Nonlinear system; Artificial intelligence; Discriminant; Pattern recognition (psychology); Computer science; Psychology; Machine learning; Mathematics; Econometrics; Statistics; Medicine; Pathology; Physics","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":[0.006156311,0.0007054785,0.00090658,0.001566095,0.000422962,0.0009821553,0.001035875,0.0006882127,0.001015273],"category_scores_gemma":[0.01555287,0.0002646087,0.001020632,0.001158205,0.001340213,0.000842756,0.001673389,0.002161567,0.0002777904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007287425,"about_ca_system_score_gemma":0.001112164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001768583,"about_ca_topic_score_gemma":0.002174914,"domain_scores_codex":[0.9982283,0.001127354,0.00007222528,0.000247111,0.0002567294,0.00006843072],"domain_scores_gemma":[0.9919811,0.005481966,0.000956307,0.0008244063,0.0005958803,0.0001603079],"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":[0.0003048267,0.0002128361,0.01254886,0.0002630041,0.0002653682,0.0004296234,0.0005298574,0.5034137,0.01069262,0.228024,0.004430201,0.238885],"study_design_scores_gemma":[0.00001744899,0.00004536256,0.001519713,0.00001401889,0.000009556308,0.00004306801,0.0000313221,0.9267247,0.0005017085,0.0700172,0.001060332,0.00001561517],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0206683,0.0001917953,0.9781665,0.0004536559,0.00001846314,0.00002304508,0.0001247735,0.0001002488,0.000253257],"genre_scores_gemma":[0.4607179,0.000455616,0.5355769,0.0002453475,0.0001577708,0.0002999191,0.0007028413,0.000100116,0.001743545],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006156311,"threshold_uncertainty_score":0.03255808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1973596199027446,"score_gpt":0.4291664459456224,"score_spread":0.2318068260428778,"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."}}