{"id":"W4299960305","doi":"10.17615/e7yz-n749","title":"Multi-Task Linear Programming Discriminant Analysis for the Identification of Progressive MCI Individuals","year":2020,"lang":"en","type":"article","venue":"UNC Libraries","topic":"Advanced Algorithms and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; University of California, San Diego; National Institutes of Health; Genentech; IXICO; National Institute of Biomedical Imaging and Bioengineering; University of California, Los Angeles; U.S. Food and Drug Administration; National Cancer Institute; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; Alzheimer's Association; Amorfix Life Sciences; Alzheimer's Disease Neuroimaging Initiative; F. Hoffmann-La Roche; Medpace; Elan; Novartis; AstraZeneca; Eli Lilly and Company; Bristol-Myers Squibb; Synarc; Foundation for the National Institutes of Health","keywords":"Linear discriminant analysis; Identification (biology); Task (project management); Artificial intelligence; Computer science; Discriminant; Pattern recognition (psychology); Statistics; Machine learning; Mathematics; Engineering; 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":[0.004403653,0.001610436,0.001533807,0.001427762,0.0005879962,0.0009490742,0.001439382,0.001081484,0.001727297],"category_scores_gemma":[0.007251036,0.0003688002,0.001487328,0.001069484,0.0005580585,0.0007813984,0.001252926,0.002521798,0.000948058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007917412,"about_ca_system_score_gemma":0.001758897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007630098,"about_ca_topic_score_gemma":0.004957324,"domain_scores_codex":[0.998091,0.0008813411,0.0000861712,0.0004636514,0.0003203405,0.0001574767],"domain_scores_gemma":[0.9976254,0.001255308,0.0001844996,0.0001999755,0.0006147025,0.0001201018],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001207417,0.000727563,0.01088784,0.0003046468,0.0004443631,0.0002728173,0.0002221948,0.3791583,0.005079122,0.005853833,0.01861657,0.5772253],"study_design_scores_gemma":[0.0000224233,0.00004437112,0.0009103799,0.00001043268,0.00001772934,0.00002020291,0.00002004871,0.9949326,0.0004232366,0.003032022,0.0005560425,0.00001054962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08152092,0.0028277,0.909759,0.001170526,0.0001706201,0.0001938346,0.0008056908,0.001840969,0.001710706],"genre_scores_gemma":[0.6895878,0.000958413,0.2979982,0.0005743838,0.0002615702,0.0005382653,0.004589637,0.0002868857,0.005204814],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007630098,"threshold_uncertainty_score":0.02328902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03383634787583265,"score_gpt":0.2686873847817621,"score_spread":0.2348510369059295,"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."}}