{"id":"W4280547844","doi":"10.1002/aisy.202100284","title":"Efficient and Explainable Deep Neural Networks for Airway Symptom Detection in Support of Wearable Health Technology","year":2022,"lang":"en","type":"article","venue":"Advanced Intelligent Systems","topic":"Voice and Speech Disorders","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research; Canada Research Chairs; Centre for Research on Brain, Language and Music","keywords":"Computer science; Wearable computer; Microphone; Wearable technology; Artificial neural network; Deep neural networks; Software deployment; Transparency (behavior); Artificial intelligence; Machine learning; Speech recognition; Human–computer interaction; Embedded system; Computer security","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":[0.0004246322,0.0001288089,0.0004151048,0.0003320679,0.0001482663,0.00000543719,0.00007639025,0.00006145178,0.00001884207],"category_scores_gemma":[0.00004796833,0.0001285648,0.00005590812,0.0004708642,0.0000428964,0.00003140763,0.00005211929,0.0002202535,0.00000118382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002370659,"about_ca_system_score_gemma":0.00004374878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001517453,"about_ca_topic_score_gemma":0.00007653562,"domain_scores_codex":[0.9985997,0.00004970855,0.0004976942,0.0002925953,0.0001741223,0.0003861726],"domain_scores_gemma":[0.999391,0.00006152855,0.000189232,0.0002208385,0.00006558335,0.00007179114],"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":[0.0004486836,0.0002707915,0.003432103,0.0005883984,0.00003498965,0.00001027626,0.0006070232,0.939222,0.001489247,0.000450671,0.00008919963,0.05335655],"study_design_scores_gemma":[0.002157427,0.004631145,0.0001781868,0.0001300647,0.00002889959,0.0002030255,0.0214655,0.9450854,0.004980674,0.00006394977,0.02083898,0.0002367542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8340368,0.01451491,0.1453986,0.001202153,0.001051952,0.003461414,0.00001144593,0.0001163213,0.0002063912],"genre_scores_gemma":[0.9986072,0.0002433414,0.0001568563,0.0001680016,0.0000232138,0.0004695323,0.0000140976,0.00002372276,0.0002940325],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1645704,"threshold_uncertainty_score":0.5242721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01226057076743615,"score_gpt":0.2796479018792254,"score_spread":0.2673873311117893,"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."}}