{"id":"W2167040761","doi":"10.1109/acssc.2005.1599812","title":"Multiple Description Conjugate Vector Quantizers with Side Distortion Compensation","year":2006,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Decoding methods; Speech coding; Algorithm; Erasure; Computational complexity theory; Channel (broadcasting); Coding (social sciences); Companding; Distortion (music); Speech recognition; Theoretical computer science; Mathematics; Telecommunications; Orthogonal frequency-division multiplexing; Statistics; Bandwidth (computing)","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":[0.001781992,0.0006522518,0.0009795919,0.0005001849,0.0003490073,0.001012184,0.001571799,0.0009521596,0.003385496],"category_scores_gemma":[0.004074153,0.000321253,0.0004543489,0.0012245,0.0005755589,0.001740502,0.001209583,0.001026127,0.0009357794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005944349,"about_ca_system_score_gemma":0.001035671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000942523,"about_ca_topic_score_gemma":0.001502191,"domain_scores_codex":[0.9987282,0.000325998,0.0001246627,0.0001647194,0.0005967714,0.00005970188],"domain_scores_gemma":[0.9983675,0.0006473953,0.0002111032,0.0003693172,0.0003669436,0.00003770981],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005461112,0.0001097695,0.0008539614,0.0003649989,0.000102402,0.0001390115,0.0001446401,0.1683251,0.03855522,0.1092454,0.005863767,0.6757496],"study_design_scores_gemma":[0.0001305577,0.0002318312,0.0003694338,0.00002558516,0.00003276458,0.0002374291,0.00002385618,0.9255246,0.04439202,0.01890185,0.0100699,0.00006027173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004501531,0.0004294036,0.9937916,0.0001233571,0.00004730827,0.00005911414,0.00008086914,0.000279417,0.000687408],"genre_scores_gemma":[0.2654249,0.0006012374,0.7262639,0.0003270149,0.0001048076,0.0002547817,0.0005362295,0.00008185,0.006405244],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003385496,"threshold_uncertainty_score":0.0113256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01657206472070169,"score_gpt":0.2324959308843234,"score_spread":0.2159238661636217,"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."}}