{"id":"W1953727112","doi":"10.1109/icassp.1995.479794","title":"Fast and low-complexity LSF quantization using algebraic vector quantizer","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Codebook; Vector quantization; Rounding; Algorithm; Linde–Buzo–Gray algorithm; Computer science; Quantization (signal processing); Computational complexity theory; Encoding (memory); Algebraic number; Mathematics; Theoretical computer science; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001008611,0.0001453525,0.0001551681,0.0001110166,0.0001667156,0.0001453508,0.000503288,0.00005347869,0.0001813274],"category_scores_gemma":[0.00004328314,0.0001275247,0.00002684789,0.0003354077,0.00008649143,0.001183492,0.0004537872,0.00009635271,0.00004243791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002480645,"about_ca_system_score_gemma":0.000007950947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003550814,"about_ca_topic_score_gemma":0.000006763102,"domain_scores_codex":[0.9988438,0.00005930934,0.0002177111,0.0004323378,0.0002163723,0.0002304038],"domain_scores_gemma":[0.9990996,0.00005794513,0.00008989511,0.0006028861,0.00006036893,0.00008934054],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007043921,0.0002413081,0.001728229,0.00007245877,0.00001453809,0.00002170274,0.0005490282,0.0002229675,0.09778839,0.7951298,0.006445288,0.09777926],"study_design_scores_gemma":[0.0002395802,0.00004109742,0.003113781,0.00005238299,0.000003560911,0.00003189296,0.00001147626,0.9463039,0.0301792,0.01875269,0.0009460857,0.0003243267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03210266,0.0001242247,0.965886,0.0002754676,0.0001173102,0.0001432106,0.000003939858,0.0006448434,0.0007023241],"genre_scores_gemma":[0.5448468,0.00003404195,0.4546541,0.0002790186,0.00002753055,0.000003863521,0.00000315735,0.00001038383,0.0001410599],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.946081,"threshold_uncertainty_score":0.5200307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07185963094802084,"score_gpt":0.2926500524686987,"score_spread":0.2207904215206779,"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."}}