{"id":"W2810027428","doi":"10.1177/2331216518781746","title":"Coherent Coding of Enhanced Interaural Cues Improves Sound Localization in Noise With Bilateral Cochlear Implants","year":2018,"lang":"en","type":"article","venue":"Trends in Hearing","topic":"Hearing Loss and Rehabilitation","field":"Neuroscience","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Canada Research Chairs; FP7 Health; H2020 European Research Council; European Regional Development Fund; Deutsche Forschungsgemeinschaft","keywords":"QUIET; Cochlear implant; Interaural time difference; Sound localization; Coding (social sciences); Noise (video); Computer science; Speech recognition; Azimuth; Binaural recording; Intelligibility (philosophy); Speech perception; Acoustics; Audiology; Perception; Mathematics; Artificial intelligence; Psychology; Physics; Medicine; Neuroscience","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001635774,0.0001053975,0.0001794401,0.0003172269,0.00005347031,0.00003331435,0.00009234984,0.00004871753,0.0000298339],"category_scores_gemma":[0.00008825312,0.00008442486,0.00002203713,0.0004556817,0.0001488982,0.0002026236,0.00004817594,0.000119561,0.000004673588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000770702,"about_ca_system_score_gemma":0.00001523596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004341735,"about_ca_topic_score_gemma":0.0005397287,"domain_scores_codex":[0.9989333,0.00007317,0.0003101579,0.0002866079,0.0001534402,0.0002432824],"domain_scores_gemma":[0.9996212,0.0001035417,0.00007233956,0.0001393357,0.00002807527,0.00003552027],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001784837,0.00007767391,0.08198418,0.00006345499,0.000001722185,0.000004567153,0.006081455,0.0005964589,0.8990943,0.0001495054,0.000004169162,0.01176397],"study_design_scores_gemma":[0.0008873591,0.0005191925,0.5077485,0.000475754,0.000002886768,0.000008245832,0.0002550593,0.0207715,0.4688886,0.0002708065,0.000007141533,0.0001649498],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998389,0.000005345016,0.000557491,0.0000292502,0.0001536422,0.0001006156,0.000001990941,0.00002849866,0.0007341883],"genre_scores_gemma":[0.999628,0.000003464146,0.0002065436,0.0000364491,0.00003705956,0.00001063406,0.00000211514,0.0000132685,0.00006243799],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4302057,"threshold_uncertainty_score":0.3442746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04910927379777547,"score_gpt":0.3293605610969014,"score_spread":0.2802512872991259,"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."}}