{"id":"W4403129466","doi":"10.1038/s41598-024-73225-x","title":"Using cross-recurrence quantification analysis to compute similarity measures for time series of unequal length with applications to sleep stage analysis","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"York University; Deutsche Forschungsgemeinschaft; University of Washington; Leuphana Universität Lüneburg; Case Western Reserve University; Johns Hopkins University; National Heart, Lung, and Blood Institute; University of California, Davis; University of Minnesota","keywords":"Ultradian rhythm; Computer science; Time series; Polysomnography; Series (stratigraphy); Categorical variable; Non-rapid eye movement sleep; Statistics; Sleep Stages; Data mining; Sleep (system call); Artificial intelligence; Pattern recognition (psychology); Machine learning; Medicine; Mathematics; Circadian rhythm; Eye movement; Biology; Apnea; Internal medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002739554,0.0002216059,0.0005563147,0.00158487,0.0006138062,0.001907445,0.000607038,0.00005533731,0.00003594832],"category_scores_gemma":[0.0001043496,0.0001898228,0.0004388464,0.01605995,0.0001789582,0.000758721,0.0002280229,0.00008326537,0.000009932864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008095616,"about_ca_system_score_gemma":0.0001970797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001402233,"about_ca_topic_score_gemma":0.0003334127,"domain_scores_codex":[0.996246,0.00005822744,0.0009132425,0.001563701,0.0008358336,0.000382932],"domain_scores_gemma":[0.996498,0.00009958354,0.0004214223,0.001694827,0.001067624,0.0002184989],"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":[0.00001973348,0.00008883761,0.005979924,0.00008305261,0.002288953,0.00001923686,0.001198657,0.9614146,0.008301696,0.006038555,0.0002296034,0.01433712],"study_design_scores_gemma":[0.00003150872,0.00006666298,0.002016331,0.00002654897,0.001612476,0.000006814999,0.00005955178,0.9754027,0.007393023,0.0006222191,0.01244492,0.0003172203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07854687,0.0001015159,0.9199992,0.0001421403,0.0003784769,0.0005613907,0.00006675611,0.0001274357,0.00007622003],"genre_scores_gemma":[0.8028511,6.620435e-7,0.1958066,0.00001180405,0.00003462159,0.0000742928,0.0001179489,0.00001193489,0.001090988],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7243043,"threshold_uncertainty_score":0.9991287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05408167194127056,"score_gpt":0.3254731875311681,"score_spread":0.2713915155898975,"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."}}