{"id":"W2130279916","doi":"10.1109/tit.2004.833337","title":"Efficient Source Decoding Over Memoryless Noisy Channels Using Higher Order Markov Models","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Decoding methods; Redundancy (engineering); Algorithm; Encoder; Computer science; Markov process; Computational complexity theory; Markov chain; Maximum a posteriori estimation; A priori and a posteriori; Channel (broadcasting); Mathematics; Telecommunications; Maximum likelihood; 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":[0.0002452103,0.0002260903,0.000172438,0.0003812776,0.0002668689,0.00006992265,0.0002504317,0.000135997,0.0001398131],"category_scores_gemma":[0.00000332593,0.0002450772,0.0000843732,0.0004027657,0.00006593044,0.0009689875,0.00000344078,0.0003333443,0.00006365996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003875691,"about_ca_system_score_gemma":0.00002819277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001173056,"about_ca_topic_score_gemma":0.000001519236,"domain_scores_codex":[0.9989229,0.00004104698,0.0004352827,0.000106018,0.0002360103,0.0002587383],"domain_scores_gemma":[0.9991638,0.00008238704,0.00008919748,0.0004821843,0.0001058587,0.00007657383],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002271256,0.0000309797,7.167637e-8,0.00003583372,0.00002647818,1.805215e-7,0.001159677,0.9727244,0.0008461779,0.004897415,0.000008933043,0.02024712],"study_design_scores_gemma":[0.0006158555,0.00001723908,0.00000197267,0.0001114818,0.0000226071,0.000007330665,0.0002771554,0.9203789,0.07231425,0.005457395,0.0004483593,0.0003474636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0382334,0.00003591212,0.9572045,0.00002528315,0.0006054152,0.0003004149,0.00001792834,0.001162441,0.002414705],"genre_scores_gemma":[0.9790117,0.00004906125,0.02051295,0.0001874385,0.0000256986,0.00008551886,0.000006490805,0.00004578716,0.00007540328],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9407783,"threshold_uncertainty_score":0.999396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01459078337829266,"score_gpt":0.2393267624969861,"score_spread":0.2247359791186934,"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."}}