{"id":"W2064937918","doi":"10.1016/j.neunet.2008.06.012","title":"Variational Bayesian least squares: An application to brain–machine interface data","year":2008,"lang":"en","type":"article","venue":"Neural Networks","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; York University","funders":"National Institute of Neurological Disorders and Stroke","keywords":"Overfitting; Computer science; Artificial intelligence; Neurophysiology; Bayesian probability; Brain–computer interface; Linear model; Machine learning; Linear regression; Pattern recognition (psychology); Artificial neural network; Electroencephalography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001711616,0.0002157418,0.000180068,0.00007638852,0.0003078796,0.0001083019,0.001388875,0.00008493574,0.00007054352],"category_scores_gemma":[0.0001348977,0.0001962795,0.00003617456,0.0003755606,0.00008266852,0.0006270821,0.0004648945,0.000323764,0.00006245502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002685809,"about_ca_system_score_gemma":0.00002106439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006064528,"about_ca_topic_score_gemma":0.00007914133,"domain_scores_codex":[0.9979351,0.0001840024,0.0003006845,0.0008941483,0.000311093,0.000374963],"domain_scores_gemma":[0.9983346,0.0003233067,0.00009895155,0.0009847281,0.0000372033,0.0002212059],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003256005,0.0005302152,0.003661453,0.00001962486,0.00001757462,0.00006428632,0.001482204,0.772439,0.05399413,0.002064323,0.06170855,0.1036931],"study_design_scores_gemma":[0.0002300734,0.0001823432,0.002147406,0.00001027412,0.000003876696,0.0001339707,0.0000102773,0.9850506,0.001779508,0.00007849185,0.01015079,0.0002224511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1677074,0.00006678226,0.8248863,0.00529447,0.0006934769,0.0004529832,0.00008323383,0.0002955481,0.0005198105],"genre_scores_gemma":[0.9907547,0.000007019443,0.001311231,0.006793795,0.0007215776,0.00001954286,0.0001116394,0.00003042543,0.0002501183],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8235751,"threshold_uncertainty_score":0.8004046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04205080426875784,"score_gpt":0.304301683152532,"score_spread":0.2622508788837742,"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."}}