{"id":"W4297804413","doi":"10.1109/sas54819.2022.9881371","title":"Impact of face covering models on respiratory sound classification applications","year":2022,"lang":"en","type":"article","venue":"","topic":"Music and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Élisabeth Bruyère Hospital; National Research Council Canada; Carleton University","funders":"National Research Council; University of Ottawa","keywords":"Computer science; Classifier (UML); Elbow; Speech recognition; Artificial intelligence; Facial recognition system; Robustness (evolution); Pattern recognition (psychology); Face (sociological concept); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001884887,0.001177501,0.0005526398,0.0004981174,0.0003333709,0.001157123,0.0005229603,0.001099923,0.001767022],"category_scores_gemma":[0.006393507,0.0002566296,0.0005751037,0.0002395324,0.0004189071,0.001126933,0.000860077,0.0009434233,0.001055304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005392699,"about_ca_system_score_gemma":0.00049671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00641931,"about_ca_topic_score_gemma":0.004966537,"domain_scores_codex":[0.9990662,0.0002849545,0.00006628995,0.000232902,0.0002279055,0.0001217427],"domain_scores_gemma":[0.9976221,0.001523616,0.0001138072,0.0002342307,0.0004375197,0.00006879414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001578046,0.0004254488,0.01646947,0.0002567077,0.0002609179,0.0004492948,0.0003365939,0.4834023,0.06074227,0.0009353572,0.003577362,0.4315663],"study_design_scores_gemma":[0.000008832281,0.000297787,0.004120956,0.00003732553,0.0000365204,0.0001169121,0.0001145934,0.9743428,0.01933457,0.0005340467,0.001028975,0.00002669151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8265786,0.001135282,0.1611494,0.0006998371,0.0005290785,0.0001607913,0.0005862915,0.003286319,0.005874481],"genre_scores_gemma":[0.9674867,0.0002124525,0.029144,0.0001506979,0.0000433962,0.00004923379,0.0006741667,0.00008918081,0.002150191],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00641931,"threshold_uncertainty_score":0.01276386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09150591382574236,"score_gpt":0.3219475496372853,"score_spread":0.2304416358115429,"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."}}