{"id":"W1875208079","doi":"10.1109/icassp.1988.196603","title":"Multiply occupied cells (speech codecs)","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Tellabs (Canada)","funders":"","keywords":"Concatenation (mathematics); Codec; Cardinality (data modeling); Computer science; Distortion (music); Speech recognition; Algorithm; Theoretical computer science; Mathematics; Discrete mathematics; Combinatorics; Telecommunications; Data mining","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":[0.0007379123,0.001154095,0.0007461247,0.001658203,0.001091225,0.002761634,0.001429873,0.001818484,0.009961716],"category_scores_gemma":[0.005705642,0.0005372618,0.000505261,0.001683878,0.002446958,0.002901593,0.002872891,0.002222768,0.006573644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007391449,"about_ca_system_score_gemma":0.0009395847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001152774,"about_ca_topic_score_gemma":0.001347006,"domain_scores_codex":[0.9982742,0.0003441926,0.0001186547,0.000235307,0.0008226773,0.0002050072],"domain_scores_gemma":[0.9962288,0.001495794,0.0003619862,0.0009313352,0.0008483737,0.0001336849],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004575691,0.00007147673,0.0006813141,0.0003424355,0.00004448257,0.001530861,0.0006832075,0.01700912,0.04232284,0.5922749,0.01813029,0.3264515],"study_design_scores_gemma":[0.00008374264,0.0004935562,0.0008127366,0.0003152609,0.0000902207,0.006375187,0.0003728647,0.1958317,0.1621026,0.2910615,0.3422456,0.0002149859],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01352332,0.001379322,0.9584879,0.0004081394,0.0008115607,0.0001652947,0.0004203557,0.002398049,0.02240607],"genre_scores_gemma":[0.2761271,0.001921973,0.6761114,0.001071144,0.0009259452,0.0005424498,0.001126902,0.001171323,0.0410018],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009961716,"threshold_uncertainty_score":0.03332531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0146610528278227,"score_gpt":0.2659821407176194,"score_spread":0.2513210878897967,"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."}}