{"id":"W2017689092","doi":"10.1016/j.jneumeth.2015.01.022","title":"Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines","year":2015,"lang":"en","type":"article","venue":"Journal of Neuroscience Methods","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":317,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"Université de Lyon; Canada Research Chairs; Fondation Fyssen; Agence Nationale de la Recherche","keywords":"Support vector machine; Computer science; Artificial intelligence; Pattern recognition (psychology); Linear discriminant analysis; Decision tree; Feature selection; Random forest; Machine learning; Cluster analysis; Sleep Stages; Sensitivity (control systems); Feature (linguistics); Class (philosophy); Tree (set theory); Electroencephalography; Polysomnography; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004213107,0.0002973543,0.0004970856,0.0005330677,0.0003969981,0.0004858235,0.0008461666,0.00008505283,0.00001026],"category_scores_gemma":[0.01381306,0.0002269534,0.0001367398,0.0007968083,0.0003611666,0.001321547,0.0002936014,0.000668339,0.000003073344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009318988,"about_ca_system_score_gemma":0.0001768722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008762909,"about_ca_topic_score_gemma":0.000002618721,"domain_scores_codex":[0.9955355,0.001632288,0.0009470565,0.0005798898,0.0008559212,0.0004493639],"domain_scores_gemma":[0.996206,0.001818823,0.001069474,0.0002854192,0.0002108389,0.0004094781],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002688656,0.00008270307,0.002580858,0.00002263693,0.000002520392,0.00007950555,0.0008471061,0.006148042,0.8448282,0.00004838064,0.00002707132,0.1453061],"study_design_scores_gemma":[0.0006821227,0.0005421497,0.01556342,0.00008592456,0.00002216774,0.00117534,0.0001559496,0.944256,0.03567854,0.0002068207,0.00139629,0.0002353016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.691899,0.00007071575,0.3055305,0.0003584877,0.001878108,0.0001234344,0.000002587388,0.00005564248,0.00008154416],"genre_scores_gemma":[0.7365837,0.0000278347,0.2622233,0.0007868061,0.000156016,0.000001732843,1.545696e-7,0.00003087403,0.0001896323],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.938108,"threshold_uncertainty_score":0.994494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1751237062647207,"score_gpt":0.4330076272604829,"score_spread":0.2578839209957623,"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."}}