{"id":"W1987043386","doi":"10.1109/cwit.2013.6621594","title":"An achievability proof for the lossy coding of Markov sources with feed-forward","year":2013,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Lossy compression; Ergodic theory; Markov chain; Source code; Markov process; Computer science; Algorithm; Shannon–Fano coding; Variable-length code; Theoretical computer science; Coding (social sciences); Binary number; Mathematics; Discrete mathematics; Decoding methods; Statistics; Arithmetic","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":[0.0002593418,0.00009356425,0.0001294096,0.00003133516,0.00006380151,0.00004066679,0.0004338029,0.00004638445,0.00008351256],"category_scores_gemma":[0.00002012691,0.00005752317,0.0000351915,0.00008841397,0.00009496937,0.0002105673,0.00003764203,0.00009892388,0.000002005055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002086303,"about_ca_system_score_gemma":0.000008086429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001154049,"about_ca_topic_score_gemma":0.0000984361,"domain_scores_codex":[0.999477,0.0000257732,0.000176852,0.0000905673,0.0001021035,0.0001276943],"domain_scores_gemma":[0.9988967,0.0002211536,0.00003658198,0.000704225,0.0001083391,0.0000330633],"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":[0.0003524076,0.00134723,0.1323613,0.004604525,0.001243834,5.517115e-7,0.02434352,0.02292777,0.1974249,0.08856206,0.02324422,0.5035877],"study_design_scores_gemma":[0.0005796997,0.0003858901,0.03281163,0.0001072118,0.00005337803,0.000004277275,0.001909217,0.4951797,0.4579017,0.004194187,0.006345874,0.0005272652],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7384415,0.0001460152,0.2554222,0.000576475,0.00001758527,0.001349494,0.000008120461,0.000642588,0.003396056],"genre_scores_gemma":[0.972569,0.00002222753,0.02700843,0.00002527495,0.0000142824,0.0002990071,0.000004054703,0.00002132648,0.00003634812],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5030604,"threshold_uncertainty_score":0.2345727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01048880119577621,"score_gpt":0.2365397556451853,"score_spread":0.2260509544494091,"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."}}