{"id":"W4385066827","doi":"10.3389/fauot.2023.1215965","title":"Using machine learning to assist auditory processing evaluation","year":2023,"lang":"en","type":"article","venue":"Frontiers in Audiology and Otology","topic":"Hearing Loss and Rehabilitation","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Ontario","keywords":"Audiologist; Machine learning; Categorization; Artificial intelligence; Test (biology); Population; Computer science; Random forest; Feature (linguistics); Psychology; Medicine; Audiology; Hearing loss","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":[0.003781635,0.001030997,0.0005408185,0.001929545,0.000275397,0.001477946,0.0006572423,0.0008182022,0.004136865],"category_scores_gemma":[0.01104601,0.0001879133,0.0005912117,0.0007282111,0.0002649164,0.0009354713,0.0007326911,0.000666097,0.001870908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005545943,"about_ca_system_score_gemma":0.000540927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002257938,"about_ca_topic_score_gemma":0.002594472,"domain_scores_codex":[0.9981052,0.0007708408,0.0001570635,0.0003187059,0.0005463755,0.0001018167],"domain_scores_gemma":[0.9947723,0.002917588,0.0003870747,0.0003364303,0.001447082,0.0001395896],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003431611,0.0002568004,0.04120286,0.0002755361,0.0001800401,0.0001582117,0.0001345296,0.04535931,0.01054366,0.0008122502,0.007561087,0.8931726],"study_design_scores_gemma":[0.00002783052,0.0004095692,0.02656675,0.0001576282,0.00007769881,0.0002889183,0.0001367,0.9460091,0.01649524,0.00439598,0.005385084,0.00004936949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2775876,0.002603885,0.6985612,0.001061308,0.0003222511,0.0004837852,0.002537739,0.008190218,0.008652014],"genre_scores_gemma":[0.8577081,0.0004774667,0.1379696,0.0001681856,0.00009701435,0.00018164,0.001410527,0.0001127748,0.001874648],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004136865,"threshold_uncertainty_score":0.01999944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06327527381865047,"score_gpt":0.3465655192849197,"score_spread":0.2832902454662692,"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."}}