{"id":"W2952200543","doi":"10.48550/arxiv.1410.1013","title":"Assess Sleep Stage by Modern Signal Processing Techniques","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Science Council; European Commission","keywords":"SIGNAL (programming language); Sleep (system call); Pattern recognition (psychology); Artificial intelligence; Computer science; Electroencephalography; Signal processing; Support vector machine; Sleep Stages; Speech recognition; Polysomnography; Psychology; 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":[0.0006408163,0.0006182103,0.0003020744,0.002217924,0.0001474564,0.0005954789,0.000281364,0.0005503742,0.001515156],"category_scores_gemma":[0.002517858,0.0001272457,0.0003453209,0.0009604425,0.0003197503,0.0009068068,0.0003164773,0.0003338458,0.0005497359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001283124,"about_ca_system_score_gemma":0.0001564751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004799002,"about_ca_topic_score_gemma":0.0007458268,"domain_scores_codex":[0.9997185,0.00006685356,0.00003512877,0.00008154266,0.00008250763,0.00001549096],"domain_scores_gemma":[0.9994267,0.000233597,0.0001286444,0.00006984545,0.0001183774,0.00002279755],"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":[0.0009764557,0.0001734603,0.08766468,0.0005322575,0.0003538715,0.0003665154,0.0005858066,0.02061935,0.1792266,0.00448608,0.001601287,0.7034137],"study_design_scores_gemma":[0.000107092,0.002183284,0.5375164,0.0001742357,0.0003458138,0.003278003,0.0008574014,0.3553072,0.07528665,0.01746218,0.007249011,0.0002327168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.431103,0.001733866,0.5603878,0.0002971201,0.0001013703,0.0001976702,0.001096735,0.0009697349,0.004112714],"genre_scores_gemma":[0.839571,0.0009844926,0.1573927,0.0000942895,0.0001145128,0.0001039944,0.000707869,0.00007201475,0.0009591882],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002217924,"threshold_uncertainty_score":0.00506866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08904782223098398,"score_gpt":0.2274578849342727,"score_spread":0.1384100627032887,"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."}}