{"id":"W2172119180","doi":"10.1109/tbme.2010.2061846","title":"Automatic and Unsupervised Snore Sound Extraction From Respiratory Sound Signals","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Obstructive Sleep Apnea Research","field":"Medicine","cited_by":115,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Sound (geography); Microphone; Computer science; Speech recognition; Respiratory sounds; Acoustics; Bioacoustics; Noise (video); Ambient noise level; Artificial intelligence; Medicine; Sound pressure; Physics; Telecommunications","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002516835,0.0002300606,0.0002967546,0.0004397904,0.0001413959,0.00004995899,0.0001005422,0.0002966388,0.001273467],"category_scores_gemma":[0.00007882772,0.0002134096,0.000101548,0.0003800993,0.0002358051,0.0001649631,0.000002537324,0.001165038,0.00008045274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009355905,"about_ca_system_score_gemma":0.00004434815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003575677,"about_ca_topic_score_gemma":0.0000181499,"domain_scores_codex":[0.9982612,0.0000291467,0.000331404,0.0003867112,0.0006263956,0.0003651045],"domain_scores_gemma":[0.9986428,0.0003910814,0.00003425557,0.0003358785,0.00006976773,0.0005262104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007658968,0.0003193658,0.0001250407,0.0001279516,0.0002226463,0.0000796812,0.0002282326,0.0002724284,0.8961133,0.0000193638,0.00003058422,0.1023848],"study_design_scores_gemma":[0.01046351,0.001564896,0.02931819,0.0004020408,0.0006875102,0.0004378151,0.0004126659,0.652113,0.2943738,0.001029832,0.007825051,0.001371716],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6895878,0.00004490744,0.3088671,0.0002760677,0.0007033111,0.0002436099,0.00003322553,0.0002116063,0.0000323446],"genre_scores_gemma":[0.9949572,0.000009678707,0.004489424,0.0001059531,0.000285258,0.00005709488,0.00001064399,0.00005341411,0.00003135185],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6518406,"threshold_uncertainty_score":0.9996395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02059978275286847,"score_gpt":0.2910696019455344,"score_spread":0.2704698191926659,"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."}}