{"id":"W4386547708","doi":"10.58190/icat.2023.13","title":"Prediction of Sleep Health Status, Visualization and Analysis of Data","year":2023,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Advanced Technologies","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Transport Canada","funders":"","keywords":"Support vector machine; Random forest; Logistic regression; Insomnia; Sleep (system call); Artificial intelligence; Sleep Stages; Sleep apnea; Computer science; Machine learning; Statistics; Affect (linguistics); Pattern recognition (psychology); Regression analysis; Data mining; Polysomnography; Psychology; Mathematics; Medicine; Apnea; Psychiatry","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":[0.0002745804,0.00005992499,0.0001435899,0.0001730623,0.00004026937,0.000007893728,0.000540324,0.0000353196,0.00001149432],"category_scores_gemma":[0.0004809337,0.00004625256,0.00002362343,0.0007779507,0.0002218241,0.0002390911,0.0006499821,0.00006313658,6.076825e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004603429,"about_ca_system_score_gemma":0.000006770488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005698328,"about_ca_topic_score_gemma":0.000006082379,"domain_scores_codex":[0.999129,0.000003299775,0.0002590957,0.0001902952,0.0003257929,0.00009254073],"domain_scores_gemma":[0.9993326,0.00003610021,0.0004226583,0.0001335582,0.00006457254,0.00001053198],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006576538,0.00008381581,0.6459246,0.00009460087,0.0002397375,4.366158e-8,0.0008443589,0.001777906,0.09770983,0.0663317,0.0002413727,0.1866863],"study_design_scores_gemma":[0.0003575375,0.0003147277,0.4520952,0.0003463516,0.0001025737,5.864678e-7,0.01032992,0.3944756,0.1229545,0.01836651,0.0005040197,0.0001524602],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973228,0.00001449825,0.0003730748,0.0008977895,0.00006726214,0.00009803221,0.0001850944,0.0001241113,0.0009173424],"genre_scores_gemma":[0.9981758,0.0003048959,0.001430274,0.00000527731,0.000002724702,0.000004615767,0.00002679747,0.000003375784,0.00004622514],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3926977,"threshold_uncertainty_score":0.1886125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1125969776113595,"score_gpt":0.3475486035917039,"score_spread":0.2349516259803444,"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."}}