{"id":"W4416647005","doi":"10.1177/23312165251396644","title":"Objective Evaluation of a Deep Learning-Based Noise Reduction Algorithm for Hearing Aids Under Diverse Fitting and Listening Conditions","year":2025,"lang":"en","type":"article","venue":"Trends in Hearing","topic":"Hearing Loss and Rehabilitation","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Beamforming; Active listening; Noise reduction; Hearing aid; Intelligibility (philosophy); Metric (unit); Azimuth","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002146192,0.001095265,0.0004969666,0.0003120893,0.0002059267,0.0006096562,0.000946769,0.0007678999,0.001351942],"category_scores_gemma":[0.004634306,0.000313516,0.0004205612,0.0001754937,0.000269248,0.0007186075,0.0007727926,0.0005706708,0.0004661795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004732846,"about_ca_system_score_gemma":0.0006088077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003311634,"about_ca_topic_score_gemma":0.005247108,"domain_scores_codex":[0.9993362,0.0001801473,0.0000463824,0.0001569715,0.0002162392,0.0000641071],"domain_scores_gemma":[0.9985209,0.0007011709,0.00008604593,0.0000764603,0.0005608343,0.0000545476],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002137793,0.000868611,0.006156318,0.0005436163,0.000312977,0.0001430025,0.0001704365,0.3893966,0.1057789,0.0006173624,0.001199708,0.4926746],"study_design_scores_gemma":[0.00005942804,0.001193563,0.002374689,0.00002727615,0.00008071567,0.00008250216,0.00004682491,0.9622126,0.03287381,0.0002602117,0.0007626571,0.00002572939],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4899133,0.0007797341,0.5026041,0.0001979634,0.0001009302,0.0002476103,0.0002863507,0.002765612,0.003104443],"genre_scores_gemma":[0.7861584,0.0002218285,0.2092349,0.0002225073,0.0000206851,0.0001991924,0.0005410591,0.0001499562,0.003251413],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003311634,"threshold_uncertainty_score":0.01135033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0719198415530158,"score_gpt":0.3747900365520834,"score_spread":0.3028701949990676,"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."}}