{"id":"W2294778452","doi":"10.5281/zenodo.43853","title":"Monitoring Sleep With 40-Hz Assr","year":2014,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Electroencephalography; Computer science; Speech recognition; Sleep (system call); Linear discriminant analysis; Noise (video); Wakefulness; Frequency domain; Pattern recognition (psychology); SIGNAL (programming language); Quadratic classifier; Artificial intelligence; Quadratic equation; Mathematics; Psychology; Computer vision; Support vector machine; Neuroscience","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003553549,0.0005163394,0.0003404904,0.0006765492,0.0001802252,0.0005306893,0.0001639495,0.0004565972,0.004663015],"category_scores_gemma":[0.0006299825,0.0001252357,0.0002167436,0.000563559,0.0001505083,0.0003084266,0.0003106339,0.000191405,0.001724634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001012919,"about_ca_system_score_gemma":0.0001883244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009396694,"about_ca_topic_score_gemma":0.002200017,"domain_scores_codex":[0.9998521,0.00003030173,0.00001619792,0.00003492241,0.0000497641,0.00001660211],"domain_scores_gemma":[0.9998389,0.00003986927,0.00001846491,0.00001835991,0.00006548116,0.00001892459],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.004172476,0.0002443044,0.05276375,0.0008088285,0.0003696672,0.001226089,0.0004773738,0.001604452,0.4402096,0.0008547234,0.01733807,0.4799306],"study_design_scores_gemma":[0.0003246932,0.002674818,0.6871957,0.0003496589,0.0005482415,0.009892068,0.000748347,0.02652838,0.2109534,0.003386416,0.05724987,0.0001484887],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8924329,0.008520165,0.0546018,0.0009861063,0.00114393,0.0005882136,0.007839749,0.002769858,0.03111728],"genre_scores_gemma":[0.9459394,0.00379001,0.02981997,0.000345402,0.0005476723,0.000199632,0.003688792,0.0003485408,0.01532072],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004663015,"threshold_uncertainty_score":0.01559937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03965489765927,"score_gpt":0.2526047218398971,"score_spread":0.2129498241806271,"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."}}