{"id":"W2466831271","doi":"10.1016/j.jneumeth.2016.06.011","title":"A multiple hold-out framework for Sparse Partial Least Squares","year":2016,"lang":"en","type":"article","venue":"Journal of Neuroscience Methods","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; Fundação para a Ciência e a Tecnologia; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; University of California, San Diego; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; Wellcome Trust; University of Southern California; Eli Lilly and Company; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Partial least squares regression; Computer science; Artificial intelligence; Mathematics; Algorithm; Pattern recognition (psychology); Statistics","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":[0.002649894,0.0001200303,0.0003040771,0.000210871,0.0001166783,0.00005723576,0.0002463763,0.00005628406,0.00007013941],"category_scores_gemma":[0.01124037,0.00006667117,0.0002391136,0.0002353483,0.0002654764,0.000266155,0.00006174656,0.0002308257,0.000007517925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003909298,"about_ca_system_score_gemma":0.0002156818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.333611e-7,"about_ca_topic_score_gemma":3.122653e-7,"domain_scores_codex":[0.9979762,0.0003300924,0.0004211445,0.0002313541,0.0006211553,0.0004200869],"domain_scores_gemma":[0.9975678,0.001272378,0.0002571267,0.0001867003,0.000380208,0.000335749],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006579991,0.0002455912,0.03534484,0.00002265875,0.00001021343,0.00007854102,0.00008468734,8.9786e-7,0.8197302,0.0002115302,0.0003498086,0.1432631],"study_design_scores_gemma":[0.00549639,0.007458235,0.3358792,0.00061131,0.0001770628,0.0006945899,0.0001978624,0.001031056,0.5562681,0.006029418,0.08590356,0.0002531801],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2750114,0.00005248131,0.7200919,0.003592764,0.0008788696,0.0002493498,0.000004967259,0.000009723871,0.0001085631],"genre_scores_gemma":[0.7835646,0.00007439679,0.2140577,0.0009214086,0.0004768389,0.00001529843,1.120245e-7,0.000015251,0.0008743579],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5085532,"threshold_uncertainty_score":0.9970884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.153298192608195,"score_gpt":0.4860092986226248,"score_spread":0.3327111060144298,"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."}}