{"id":"W3042085370","doi":"10.1016/j.jneumeth.2020.108856","title":"Gaussian discriminative component analysis for early detection of Alzheimer’s disease: A supervised dimensionality reduction algorithm","year":2020,"lang":"en","type":"article","venue":"Journal of Neuroscience Methods","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; Florida Department of Health; National Institute on Aging; National Institutes of Health; National Science Foundation","keywords":"Dimensionality reduction; Feature selection; Discriminative model; Pattern recognition (psychology); Artificial intelligence; Computer science; Binary classification; Neuroimaging; Machine learning; Support vector machine; Psychology; Neuroscience","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":[],"consensus_categories":[],"category_scores_codex":[0.001572665,0.0001116606,0.0004261586,0.0003417086,0.0001175517,0.00002673663,0.0001246622,0.00003415737,0.00001732544],"category_scores_gemma":[0.0008250754,0.00008285184,0.000440626,0.001146519,0.0002136624,0.0002491855,0.00004774589,0.0002567082,2.909883e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003305027,"about_ca_system_score_gemma":0.0001379821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008220093,"about_ca_topic_score_gemma":1.181536e-7,"domain_scores_codex":[0.9977099,0.0005690667,0.0005132813,0.0002528137,0.0007573455,0.0001976495],"domain_scores_gemma":[0.9983222,0.0001338309,0.0003707528,0.0001155635,0.0006269132,0.0004307388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0008103153,0.0003311016,0.005349761,0.00004191443,0.0001890877,0.0000250003,0.0002870575,0.00003125468,0.9113986,0.00001192202,0.000009856447,0.08151405],"study_design_scores_gemma":[0.0008832716,0.002041824,0.7551215,0.00002553286,0.001959271,0.00002776013,0.0002320965,0.02984731,0.2095225,0.0001415215,0.0001290805,0.0000683925],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4492382,0.0001225372,0.5477204,0.00241964,0.0001741308,0.0002917189,0.000013277,0.000005476125,0.00001456008],"genre_scores_gemma":[0.9323983,0.00003353051,0.0672297,0.0001960945,0.0001029784,0.000009259533,0.000002490177,0.000007736143,0.00001991558],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7497717,"threshold_uncertainty_score":0.33786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1330680841137836,"score_gpt":0.4394986864727715,"score_spread":0.3064306023589879,"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."}}