{"id":"W2086174525","doi":"10.1109/tasl.2011.2181834","title":"Context-Based Adaptive Arithmetic Encoding of EAVQ Indices","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Audio Speech and Language Processing","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Lossless compression; Computer science; Encoding (memory); Codec; Context (archaeology); Data compression; Speech recognition; Binary number; Algorithm; Arithmetic coding; Arithmetic; Context-adaptive binary arithmetic coding; Mathematics; Artificial intelligence; Computer hardware","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":[0.0003571623,0.0004125244,0.000284725,0.0004698564,0.0002623291,0.0005771181,0.0006985405,0.0002477095,0.001158673],"category_scores_gemma":[0.002265333,0.0001307381,0.0001409522,0.0003249376,0.0003514766,0.0008689574,0.0006005996,0.0006069298,0.0003878562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003247558,"about_ca_system_score_gemma":0.0003191929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004206598,"about_ca_topic_score_gemma":0.0007337047,"domain_scores_codex":[0.999698,0.00005514778,0.00002644155,0.00005706067,0.0001400759,0.00002328504],"domain_scores_gemma":[0.9994118,0.0001684187,0.0000767308,0.0001254678,0.0001998755,0.00001765083],"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":[0.000464414,0.0000620444,0.0009675675,0.0001233334,0.00001752606,0.0001325155,0.000180742,0.02730245,0.2159783,0.04572131,0.001348173,0.7077017],"study_design_scores_gemma":[0.00009825158,0.0005346369,0.001653815,0.00009646641,0.00004041992,0.0006558509,0.00006964024,0.6614758,0.2969224,0.01546449,0.02291319,0.00007494286],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05204979,0.0006449125,0.9436401,0.0001170264,0.0001249488,0.00007960276,0.00006934143,0.0006884424,0.002585985],"genre_scores_gemma":[0.4349577,0.0003255719,0.5613998,0.0001081416,0.0001109547,0.00009802187,0.0001494002,0.000118535,0.00273186],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001158673,"threshold_uncertainty_score":0.003876209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02794219079410182,"score_gpt":0.2650357053033448,"score_spread":0.237093514509243,"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."}}