{"id":"W3119510869","doi":"10.1007/s10439-020-02651-5","title":"Automatic Respiratory Phase Identification Using Tracheal Sounds and Movements During Sleep","year":2021,"lang":"en","type":"article","venue":"Annals of Biomedical Engineering","topic":"Phonocardiography and Auscultation Techniques","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Polysomnography; Sleep apnea; Respiratory system; Sleep (system call); Medicine; Apnea; Computer science; Sleep Stages; Breathing; Audiology; Speech recognition; Anesthesia; Anatomy","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":[0.0002733406,0.00009010846,0.0002137703,0.0002375864,0.00003654959,0.00001265264,0.00003458585,0.00007616837,0.00002282252],"category_scores_gemma":[0.0001735854,0.00009209788,0.00009058268,0.0003954555,0.00005580776,0.0001019788,0.00002314207,0.0001013633,5.554711e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001104981,"about_ca_system_score_gemma":0.0000258354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003053344,"about_ca_topic_score_gemma":6.219915e-8,"domain_scores_codex":[0.9990682,0.00001742286,0.0003535193,0.0001595787,0.0002498423,0.0001514317],"domain_scores_gemma":[0.999523,0.0000285837,0.00006988877,0.0001381232,0.00009527612,0.0001450847],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001239619,0.0001783125,0.0002375658,0.000579915,0.000132866,0.00004419468,0.0001855805,0.00002335178,0.9750164,0.00004068365,0.00002367609,0.02352511],"study_design_scores_gemma":[0.001415134,0.0001301287,0.03077239,0.000374514,0.00006365604,0.00005400151,0.0001125017,0.04926944,0.9171054,0.00008793383,0.0004683999,0.0001465375],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.979662,0.0008699362,0.01905107,0.0001348743,0.0000601306,0.00009004986,0.0000103955,0.0001031728,0.00001834004],"genre_scores_gemma":[0.9972922,0.00007605837,0.002388156,0.0001280556,0.00006716324,0.000008372607,0.00002042742,0.00001239734,0.000007237084],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05791099,"threshold_uncertainty_score":0.3755642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04560022320892974,"score_gpt":0.352308229076498,"score_spread":0.3067080058675682,"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."}}