{"id":"W2079634210","doi":"10.1088/0967-3334/35/12/2343","title":"Segmentation and classification of capnograms: application in respiratory variability analysis","year":2014,"lang":"en","type":"article","venue":"Physiological Measurement","topic":"Heart Rate Variability and Autonomic Control","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Wilfrid Laurier University; Ottawa Hospital","funders":"Canadian Institutes of Health Research","keywords":"Computer science; Naive Bayes classifier; Artificial intelligence; Pattern recognition (psychology); Decision tree; Segmentation; Waveform; Capnography; Data mining; Machine learning; Support vector machine; Medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002281826,0.00007581818,0.000308997,0.0000794962,0.00002216827,0.000004080046,0.00003225092,0.0000677909,0.00001588216],"category_scores_gemma":[0.0003428861,0.00005875431,0.00006314171,0.0002960352,0.00007067144,0.00003080048,0.00001332565,0.00008537862,0.000003277025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001263368,"about_ca_system_score_gemma":0.00003244411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004872733,"about_ca_topic_score_gemma":0.00003028869,"domain_scores_codex":[0.9987485,0.0003164508,0.0003658194,0.0002766111,0.000191659,0.0001009541],"domain_scores_gemma":[0.9993944,0.00008579478,0.00009422932,0.0002426047,0.0001263451,0.00005660334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00003605438,0.000319193,0.08996399,0.00005390518,0.00005822674,4.013119e-8,0.00005968745,0.0001240469,0.8698002,0.0009114047,0.000002455665,0.03867082],"study_design_scores_gemma":[0.0005417442,0.0001668684,0.9753881,0.000009997624,0.0001400474,7.247579e-8,0.00002765372,0.01851472,0.003701925,0.001192886,0.0002632946,0.00005276212],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9757664,0.00002494008,0.02314943,0.0002946953,0.00001141641,0.0004868832,0.000001561272,0.00001883576,0.0002458611],"genre_scores_gemma":[0.999298,0.000003853464,0.0004382038,0.0001258221,0.0000248531,0.00009452107,0.00001141759,0.000002331879,9.600368e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.885424,"threshold_uncertainty_score":0.2395931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0814803197527697,"score_gpt":0.3030212084004392,"score_spread":0.2215408886476695,"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."}}