{"id":"W1971082577","doi":"10.1109/tbc.2012.2186728","title":"Segmentation of Source Symbols for Adaptive Arithmetic Coding","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Broadcasting","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Context-adaptive variable-length coding; Arithmetic coding; Adaptive coding; Context-adaptive binary arithmetic coding; Shannon–Fano coding; Lossless compression; Variable-length code; Coding (social sciences); Tunstall coding; Huffman coding; Decoding methods; Computer science; Algorithm; Sub-band coding; Source code; Segmentation; Coding tree unit; Data compression; Arithmetic; Mathematics; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003188158,0.0007879862,0.0006739072,0.0007120733,0.0004564873,0.0007255058,0.0007589068,0.0007069568,0.002599309],"category_scores_gemma":[0.002441684,0.0002260782,0.0004958097,0.001070116,0.0007950054,0.0010731,0.0008479434,0.00119832,0.001264567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005722358,"about_ca_system_score_gemma":0.0007920949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001050126,"about_ca_topic_score_gemma":0.0009533105,"domain_scores_codex":[0.9995914,0.00007299244,0.00003609052,0.00009017111,0.0001590884,0.00005022619],"domain_scores_gemma":[0.9991116,0.0003273271,0.00007965892,0.0002334737,0.0002214525,0.00002644465],"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":[0.0006543844,0.0001177064,0.001054346,0.0003045926,0.00006167281,0.0004882895,0.0005145102,0.08563291,0.4134417,0.07755458,0.003819756,0.4163555],"study_design_scores_gemma":[0.00005026868,0.0004096146,0.001788502,0.0001206227,0.00006892965,0.0009499859,0.0001157272,0.6386447,0.2660065,0.0535598,0.03819865,0.00008664973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01150101,0.000296634,0.9852574,0.00006787208,0.00007664254,0.00008868715,0.00009785227,0.0005082683,0.002105631],"genre_scores_gemma":[0.2895378,0.0006786636,0.7037727,0.0002563228,0.0001270499,0.0003925091,0.0007408695,0.0003382312,0.004155709],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002599309,"threshold_uncertainty_score":0.008695543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04780098206137449,"score_gpt":0.303580003122286,"score_spread":0.2557790210609115,"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."}}