{"id":"W2170209768","doi":"10.1109/iembs.1995.579858","title":"Cochlear implant stimulation based on vector quantization","year":2002,"lang":"en","type":"article","venue":"","topic":"Hearing Loss and Rehabilitation","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Cochlear implant; Codebook; Vector quantization; Cochlea; Passband; Formant; Computer science; Speech recognition; SIGNAL (programming language); Signal processing; Stimulation; Algorithm; Digital signal processing; Electronic engineering; Engineering; Band-pass filter; Audiology; Medicine; Neuroscience; Vowel; Psychology","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":[0.0003212855,0.0003513374,0.0005301329,0.0003043155,0.0002123665,0.0006120764,0.0006307626,0.000445556,0.003382642],"category_scores_gemma":[0.0007813635,0.0001735637,0.0002330618,0.0005171287,0.0004017843,0.0007084902,0.0005143493,0.000414371,0.0007313064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003421319,"about_ca_system_score_gemma":0.0005173563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001914752,"about_ca_topic_score_gemma":0.002012312,"domain_scores_codex":[0.9995126,0.0000986623,0.00003613185,0.00005270698,0.0002714444,0.00002858352],"domain_scores_gemma":[0.9997523,0.0001164112,0.000015293,0.00003074434,0.00007358337,0.00001168285],"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":[0.000334312,0.0000859906,0.0004710162,0.0002871342,0.0000472545,0.00009959789,0.0001005648,0.1482736,0.08462309,0.04678715,0.005060154,0.7138301],"study_design_scores_gemma":[0.00008056976,0.0003352604,0.000872295,0.00007930945,0.00003027613,0.0005226104,0.00004418927,0.9167758,0.03118571,0.03407785,0.01593294,0.00006329786],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008223221,0.002019172,0.9832737,0.0001930937,0.0001467157,0.00008000676,0.00006373399,0.000659559,0.005340733],"genre_scores_gemma":[0.534327,0.002605286,0.4534466,0.0002372046,0.0001405184,0.0002020637,0.0002626763,0.00008086691,0.008697901],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003382642,"threshold_uncertainty_score":0.01131612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05914739608189999,"score_gpt":0.2895391255197542,"score_spread":0.2303917294378542,"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."}}