{"id":"W3195163630","doi":"10.32920/ryerson.14662887.v1","title":"Audio display and environmental sound analysis of diagnostic and therapeutic respiratory sounds","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Music and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Sound (geography); Computer science; Respiratory sounds; Hidden Markov model; Speech recognition; Sound analysis; Work (physics); Redundancy (engineering); Audio signal processing; Multimedia; Human–computer interaction; Audio signal; Medicine; Engineering; Speech coding; Acoustics","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":[],"consensus_categories":[],"category_scores_codex":[0.0002558825,0.0002209909,0.0004763137,0.0002094574,0.000103458,0.0003367205,0.0003502692,0.0001415547,0.00005563549],"category_scores_gemma":[0.00004073923,0.000192274,0.000117254,0.0002506067,0.0002033657,0.0001304488,0.001453492,0.0002215315,6.602428e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003006888,"about_ca_system_score_gemma":0.00006985546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003085203,"about_ca_topic_score_gemma":0.00004462449,"domain_scores_codex":[0.9984851,0.00008487237,0.0003048597,0.0006825011,0.0002652371,0.00017745],"domain_scores_gemma":[0.9986512,0.0004653515,0.0002075291,0.0005729088,0.00001628628,0.00008669996],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001461883,0.0005055264,0.7007622,0.001101786,0.007134933,0.0001312507,0.01628469,0.00393767,0.006543798,0.008570217,0.0002179068,0.2547954],"study_design_scores_gemma":[0.0007075137,0.0001315495,0.8332931,0.0003161589,0.003516923,0.00002210825,0.0009621403,0.146729,0.001757513,0.01017924,0.0008961768,0.001488513],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7153879,0.00846336,0.2755034,0.0001066049,0.00009353382,0.00007789901,0.00000917642,0.00002498953,0.0003331723],"genre_scores_gemma":[0.9958229,0.0003201331,0.002836118,0.000858583,0.00002870235,0.00001116012,0.00001441536,0.000008578261,0.00009938787],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.280435,"threshold_uncertainty_score":0.7840706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.020861125648744,"score_gpt":0.2517764692571445,"score_spread":0.2309153436084005,"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."}}