{"id":"W3128971027","doi":"10.1016/j.neuroimage.2021.117787","title":"A Hilbert-based method for processing respiratory timeseries","year":2021,"lang":"en","type":"article","venue":"NeuroImage","topic":"Heart Rate Variability and Autonomic Control","field":"Medicine","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network","funders":"René und Susanne Braginsky Stiftung; Universität Zürich; Eidgenössische Technische Hochschule Zürich","keywords":"Preprocessor; Computer science; Estimator; Speech recognition; Pattern recognition (psychology); Artificial intelligence; Algorithm; Data mining; Statistics; Mathematics","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":[0.001179239,0.0008592817,0.0005353772,0.001179884,0.0004076646,0.001182956,0.001006669,0.0007937737,0.01315285],"category_scores_gemma":[0.004670637,0.0004208475,0.001073211,0.001084862,0.0004271857,0.001254888,0.001250783,0.001674322,0.006193838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000266931,"about_ca_system_score_gemma":0.0009324294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001450589,"about_ca_topic_score_gemma":0.002216533,"domain_scores_codex":[0.9993302,0.0001411705,0.00006051424,0.0001746065,0.0002444804,0.00004909658],"domain_scores_gemma":[0.9991042,0.000357104,0.0000754867,0.000195208,0.0002107305,0.0000572038],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002760516,0.00009772353,0.00114261,0.0003537179,0.0001699599,0.0001857903,0.0002127917,0.02782652,0.08305952,0.02515797,0.0150191,0.8464983],"study_design_scores_gemma":[0.00007364643,0.0002562311,0.005177638,0.00008872043,0.0001137344,0.001306109,0.0001232069,0.8006958,0.05679889,0.04918282,0.08598971,0.000193559],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009421885,0.0001259723,0.9971541,0.00005983497,0.00006335589,0.00002274959,0.0002056924,0.001090984,0.0003352247],"genre_scores_gemma":[0.03415986,0.0004642323,0.9585131,0.0001296529,0.0002492684,0.0001897602,0.001357518,0.001274489,0.003661984],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01315285,"threshold_uncertainty_score":0.04400063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04193711420126113,"score_gpt":0.3402079570900023,"score_spread":0.2982708428887412,"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."}}