{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003335478,0.0004418927,0.0004250529,0.001393512,0.0004905234,0.002981754,0.0007101975,0.001972641,0.005165633],"category_scores_gemma":[0.006587512,0.0001896461,0.0003084257,0.002021538,0.001962024,0.002488746,0.0009730657,0.002112334,0.001534305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001209714,"about_ca_system_score_gemma":0.001207233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001550416,"about_ca_topic_score_gemma":0.001842424,"domain_scores_codex":[0.9988574,0.0006504608,0.00004674367,0.00009852365,0.0002897603,0.0000571369],"domain_scores_gemma":[0.9923308,0.005962939,0.0001655282,0.0002206961,0.001106772,0.0002132715],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006163616,0.0002191952,0.00444151,0.00101864,0.00004679896,0.0002410177,0.0003256266,0.01074798,0.0009393754,0.1923565,0.04028143,0.7493203],"study_design_scores_gemma":[0.00003167402,0.0001486379,0.008038303,0.002625329,0.00003011592,0.0006853015,0.001075931,0.06155129,0.001956177,0.6996609,0.2241115,0.000084868],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"other","genre_scores_codex":[0.01411375,0.5651828,0.1603405,0.1775801,0.002979754,0.0001164528,0.0002268343,0.0003609807,0.07909881],"genre_scores_gemma":[0.3410077,0.4653245,0.1366038,0.008985798,0.01011596,0.0002607712,0.0002629822,0.0001417356,0.03729672],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.005165633,"threshold_uncertainty_score":0.01763988,"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."}}