{"id":"W4236129552","doi":"10.5383/ijtee.17.02.005","title":"Modelling of Hearing Aid’s Digital Signal Processor","year":2020,"lang":"en","type":"article","venue":"International Journal of Thermal and Environmental Engineering","topic":"Noise Effects and Management","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hearing aid; Audiogram; Computer science; Hearing loss; Noise (video); Filter (signal processing); Signal processing; Digital signal processing; Speech recognition; Audiology; Computer hardware; Medicine; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001858203,0.0005251727,0.0003730763,0.0003629179,0.0004054369,0.001381463,0.001299717,0.001477619,0.006450339],"category_scores_gemma":[0.0005596227,0.0002708417,0.0005549206,0.0002725142,0.0003904462,0.0008840966,0.0005487541,0.0005268877,0.002422633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004757352,"about_ca_system_score_gemma":0.0006399314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004158702,"about_ca_topic_score_gemma":0.001571269,"domain_scores_codex":[0.9997113,0.00005184465,0.00001825655,0.00006458734,0.0001256803,0.00002821152],"domain_scores_gemma":[0.9998556,0.00004673668,0.00001836137,0.00001196883,0.00006044014,0.00000690047],"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":[0.0001773558,0.00008889531,0.001803999,0.0004971838,0.00005705448,0.0008628253,0.0006361097,0.8784157,0.04873968,0.0209152,0.001736221,0.04606976],"study_design_scores_gemma":[0.00001956997,0.0001702236,0.0005950465,0.00004609774,0.00003646778,0.0003974706,0.00008308529,0.9721404,0.007922166,0.002885316,0.01568113,0.00002314042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05445648,0.0008005447,0.8967254,0.0003719071,0.0001801024,0.000218438,0.000547331,0.001620703,0.04507903],"genre_scores_gemma":[0.8619315,0.001698816,0.07442132,0.0001567585,0.00005390121,0.0005158515,0.000444295,0.0001272427,0.06065029],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006450339,"threshold_uncertainty_score":0.02157849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02774608811382273,"score_gpt":0.2622808894200127,"score_spread":0.2345348013061899,"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."}}