{"id":"W3182650744","doi":"10.22215/etd/2019-13494","title":"Machine Learning in Audiology: Applications and Implications","year":2019,"lang":"en","type":"dissertation","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Audiogram; Annotation; Reliability (semiconductor); Computer science; Audiometry; Artificial intelligence; Machine learning; Speech recognition; Hearing loss; Audiology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001077475,0.000127638,0.0001722978,0.0001944911,0.0001064514,0.0000989928,0.0003834156,0.0001408948,0.00001290806],"category_scores_gemma":[0.00002106687,0.00012072,0.00002440834,0.0003280708,0.00001126253,0.0001708156,0.00005626705,0.0003060069,0.00006569632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002276849,"about_ca_system_score_gemma":0.0001085316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002924321,"about_ca_topic_score_gemma":0.0001615114,"domain_scores_codex":[0.999153,0.00002076528,0.0001826054,0.0004108972,0.00006477812,0.000167999],"domain_scores_gemma":[0.9994555,0.00006534719,0.0001169279,0.0002742067,0.00004772988,0.00004034698],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00000483406,0.00005364078,0.03976447,0.0001935594,0.00001966309,0.000001472897,0.000775072,0.00006372226,0.006457802,0.04241639,0.0002146837,0.9100347],"study_design_scores_gemma":[0.002271439,0.0002835152,0.7016219,0.0005196427,0.00008069456,0.0001364065,0.001258033,0.02617394,0.03008289,0.09790528,0.1364502,0.003216012],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03117099,0.01245981,0.6803439,0.004222124,0.0004546591,0.001709364,0.00001031856,0.001000653,0.2686282],"genre_scores_gemma":[0.8004267,0.001752703,0.1130399,0.0008598843,0.0001262501,0.0007400333,0.001131576,0.0000599725,0.08186293],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9068187,"threshold_uncertainty_score":0.4922819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01022125884591604,"score_gpt":0.2756952511719938,"score_spread":0.2654739923260778,"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."}}