{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001923477,0.0001627651,0.0002080606,0.0002677208,0.0001680761,0.00004980733,0.0003927724,0.0000699267,0.00003802819],"category_scores_gemma":[0.00001071077,0.0001433336,0.00004817824,0.0003463306,0.0001134548,0.0006441644,0.000006402364,0.0002230836,0.000003451699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002228049,"about_ca_system_score_gemma":0.00006792507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008740797,"about_ca_topic_score_gemma":0.00003200104,"domain_scores_codex":[0.998938,0.00005575758,0.0002384373,0.000346373,0.0002163988,0.0002050107],"domain_scores_gemma":[0.9992618,0.00008850892,0.0001790287,0.000309322,0.00007359932,0.00008772005],"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":[0.00003970394,0.0001284264,0.00001475633,0.00006281628,0.00001084567,0.00002987574,0.003128624,0.00002711835,0.01380007,0.00009924457,0.000008951153,0.9826496],"study_design_scores_gemma":[0.0003362218,0.0001912147,0.00005898148,0.0003294206,0.00001670035,0.00002302076,0.0006698983,0.01803357,0.9797174,0.0003904756,0.00003415407,0.0001988949],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008369714,0.0006985061,0.9896652,0.00003141099,0.0000660535,0.0001436966,0.00001405526,0.0002842427,0.0007270916],"genre_scores_gemma":[0.7224028,0.00001430019,0.2773849,0.0001294591,0.000007079326,0.00001622463,5.203274e-7,0.000009186708,0.00003546229],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9824507,"threshold_uncertainty_score":0.5844976,"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."}}