{"id":"W1596822940","doi":"10.1109/dcc.1996.488377","title":"Audio coding using variable-depth multistage quantizers","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Sub-band coding; Computer science; Speech coding; Fidelity; Coding (social sciences); Speech recognition; Variable-length code; Quantization (signal processing); Audio signal; High fidelity; Entropy (arrow of time); Digital audio; Coding tree unit; Tunstall coding; Entropy encoding; Theoretical computer science; Algorithm; Decoding methods; Mathematics; Telecommunications; Acoustics; Statistics","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":[0.0001596955,0.0001474763,0.0001621737,0.0001158879,0.0001761477,0.0001499485,0.001004367,0.00005602692,0.0002794285],"category_scores_gemma":[0.00006722771,0.0001322313,0.00003685456,0.0004074417,0.00003784086,0.001169215,0.0005799215,0.0001371443,0.00007657136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005951439,"about_ca_system_score_gemma":0.00001158424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005204122,"about_ca_topic_score_gemma":0.000002259906,"domain_scores_codex":[0.9987167,0.00004966405,0.0002311347,0.0004335621,0.0002487335,0.0003201763],"domain_scores_gemma":[0.9987824,0.0001095622,0.00009614657,0.0008617138,0.0000499934,0.0001001858],"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":[0.000004438731,0.00017297,0.0005057871,0.00003721163,0.00002424458,0.00009516087,0.0003417881,0.001394464,0.1473271,0.7332262,0.03077229,0.08609842],"study_design_scores_gemma":[0.0002298089,0.00002199256,0.00004034812,0.00004794874,0.000002905358,0.00002962544,0.00001306093,0.9459334,0.02867142,0.002341623,0.02239077,0.0002770764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002840542,0.00006982203,0.9829087,0.00008659871,0.0001938075,0.000123707,0.000003359422,0.001231522,0.01509845],"genre_scores_gemma":[0.1349107,0.00002678282,0.8636813,0.0003215465,0.00002619093,0.000005190465,0.000001107739,0.00001242564,0.001014702],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.944539,"threshold_uncertainty_score":0.5392236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0744556640525527,"score_gpt":0.3010890016317032,"score_spread":0.2266333375791504,"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."}}