{"id":"W2117847066","doi":"","title":"A new research environment for speech testing using hearing-device processing algorithms","year":2014,"lang":"en","type":"article","venue":"Canadian acoustics","topic":"Hearing Loss and Rehabilitation","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Speech recognition; Active listening; Hearing aid; Audiogram; Speech perception; Speech processing; Noise (video); Set (abstract data type); Binaural recording; Perception; Hearing loss; Engineering; Audiology; Artificial intelligence; Psychology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0008326602,0.0001103591,0.0001206671,0.0002140075,0.0006791871,0.0001825917,0.0001883736,0.00009464296,0.00001498202],"category_scores_gemma":[0.004462379,0.0001138765,0.00002643655,0.0003450569,0.0001140241,0.0001080933,0.00003664628,0.0002444547,0.0000287663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005128108,"about_ca_system_score_gemma":0.0009780464,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02241296,"about_ca_topic_score_gemma":0.0002956296,"domain_scores_codex":[0.9983351,0.00008399197,0.0001949576,0.0003980416,0.0003120792,0.0006758723],"domain_scores_gemma":[0.9983063,0.0007679829,0.00004155368,0.0002211064,0.000104838,0.0005582717],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002691393,0.00001224742,0.0003219128,0.0001207854,0.0000010041,0.000008018827,0.0003774574,0.009052988,0.7487155,0.0002102609,0.000347283,0.2408299],"study_design_scores_gemma":[0.0003625675,0.0002118055,0.01258971,0.0001836897,0.00001725857,0.00003633966,0.0001936768,0.9687045,0.004797006,0.002212136,0.01036984,0.0003215217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7687146,0.00003872962,0.2278704,0.0007810027,0.0003020931,0.0007519523,0.00001992533,0.00006306731,0.00145823],"genre_scores_gemma":[0.8268372,0.000001621727,0.1719109,0.0003156637,0.0004177249,0.00000959956,0.00000119347,0.00003539784,0.0004706795],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9596515,"threshold_uncertainty_score":0.9840969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2255839085306958,"score_gpt":0.3690305326896241,"score_spread":0.1434466241589283,"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."}}