{"id":"W2371742128","doi":"","title":"Adaptive joint source-channel coding and MAP decoding of error correction arithmetic codes","year":2007,"lang":"en","type":"article","venue":"Journal of Communications","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Variable-length code; Decoding methods; Computer science; Algorithm; Arithmetic coding; Shannon–Fano coding; Coding gain; Adaptive coding; List decoding; Coding (social sciences); Channel (broadcasting); Source code; Theoretical computer science; Context-adaptive binary arithmetic coding; Concatenated error correction code; Mathematics; Block code; Telecommunications; Data compression; 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":[0.00115697,0.00007571948,0.0001947797,0.0002332126,0.0002428901,0.00005616964,0.0007952291,0.00004147705,0.000002503047],"category_scores_gemma":[0.0001340632,0.0000652595,0.00006138437,0.0001994639,0.00009979698,0.0004918868,0.0005285161,0.0002730651,0.00000131759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004209503,"about_ca_system_score_gemma":0.00004755603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003434432,"about_ca_topic_score_gemma":0.00002440425,"domain_scores_codex":[0.9990056,0.00009383901,0.0004959431,0.00008099825,0.0002040734,0.0001195258],"domain_scores_gemma":[0.997844,0.0005576077,0.000601327,0.0005937435,0.0003156964,0.00008756186],"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":[0.0001911605,0.001367756,0.002386333,0.0001032386,0.0003888683,0.00002911114,0.02268216,0.004097448,0.02223319,0.07020907,0.008755331,0.8675563],"study_design_scores_gemma":[0.0007574857,0.0004292575,0.01181943,0.0007094016,0.000057079,0.0004763176,0.003156018,0.9651474,0.005133644,0.006543988,0.005533046,0.0002368864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006230448,0.001202668,0.9910098,0.0007352438,0.0003393838,0.00005663213,0.00000201817,0.00001465061,0.0004091327],"genre_scores_gemma":[0.7861435,0.0003211614,0.2134189,0.00003103574,0.00003520286,6.843198e-7,0.000001009057,0.000004358826,0.00004406919],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.96105,"threshold_uncertainty_score":0.2661205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07945490248697054,"score_gpt":0.3163771763641999,"score_spread":0.2369222738772293,"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."}}