{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004241667,0.0004040112,0.001089428,0.0000986732,0.0003282972,0.0002017946,0.0008407106,0.000151232,0.0004837348],"category_scores_gemma":[0.01558377,0.00022849,0.000353538,0.0006079358,0.0006345882,0.0001864717,0.0002639707,0.000676551,0.000006887281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001169535,"about_ca_system_score_gemma":0.0003498733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003716531,"about_ca_topic_score_gemma":0.00004009253,"domain_scores_codex":[0.9954703,0.001259711,0.001151697,0.0007514098,0.0008042675,0.0005626359],"domain_scores_gemma":[0.9724768,0.02517828,0.0003263544,0.0008838196,0.0004175424,0.000717192],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.01092778,0.0006194116,0.0003848166,0.0003226123,0.00382061,0.00007204136,0.0004147212,0.001760751,0.0001111666,0.9220408,0.02441175,0.03511353],"study_design_scores_gemma":[0.001216582,0.002259715,0.01557137,0.0001539168,0.01164236,0.00003812353,0.0003436859,0.423013,0.00008146718,0.5391551,0.006018254,0.0005064138],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001137021,0.0000757582,0.9873682,0.002230577,0.0005389734,0.0007317749,0.007811365,0.00004824369,0.00005814624],"genre_scores_gemma":[0.03598322,0.000009189458,0.9624724,0.0006020812,0.0003923042,0.00008042259,0.0002690461,0.00005779908,0.000133565],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4212522,"threshold_uncertainty_score":0.9927084,"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."}}