{"id":"W1532348468","doi":"10.1007/978-3-540-30140-0_33","title":"Dynamic Shannon Coding","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Huffman coding; Shannon–Fano coding; Variable-length code; Tunstall coding; Computer science; Coding (social sciences); Context-adaptive binary arithmetic coding; Algorithm; Upper and lower bounds; Context-adaptive variable-length coding; Prefix code; Prefix; Arithmetic coding; Theoretical computer science; Data compression; Mathematics; Decoding methods; Block code; Linear code; 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":[0.0003750732,0.000705495,0.0005199591,0.001347648,0.0007873061,0.001646706,0.0006697655,0.0009743984,0.02142205],"category_scores_gemma":[0.001649752,0.0003345563,0.0003313317,0.001460561,0.001431268,0.001975595,0.001565089,0.001832323,0.005543996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007408917,"about_ca_system_score_gemma":0.0007403705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005496471,"about_ca_topic_score_gemma":0.0005485174,"domain_scores_codex":[0.999621,0.0000619066,0.00001457947,0.00005414234,0.0002070715,0.0000411905],"domain_scores_gemma":[0.9995555,0.0001489322,0.0000248202,0.0001473493,0.00009383007,0.00002966343],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002057748,0.000009990539,0.00004801889,0.00004201684,0.000005490782,0.00003936718,0.0000448049,0.003578987,0.001731623,0.9195932,0.01238717,0.06249883],"study_design_scores_gemma":[0.000009686129,0.00002270604,0.0001369491,0.00006899623,0.00001159878,0.0003217277,0.00003331203,0.02762863,0.004901294,0.8775314,0.08929989,0.0000338782],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.009950103,0.004763554,0.4559081,0.002084034,0.001575413,0.0000898237,0.0007097848,0.0009875724,0.5239316],"genre_scores_gemma":[0.412887,0.0110699,0.1569956,0.002185594,0.001766365,0.0003401234,0.001920387,0.001120453,0.4117146],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02142205,"threshold_uncertainty_score":0.07166392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01313112782771276,"score_gpt":0.2468174279906677,"score_spread":0.233686300162955,"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."}}