{"id":"W2115580915","doi":"10.1109/tsp.2007.893934","title":"Lossless Source Coding Using Nested Error Correcting Codes","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Variable-length code; Entropy encoding; Lossless compression; Algorithm; Turbo code; Computer science; Context-adaptive binary arithmetic coding; Data compression; Decoding methods; Tunstall coding; Distributed source coding; Arithmetic coding; Theoretical computer science; Low-density parity-check code","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003728649,0.0002686085,0.0002524488,0.0003303218,0.0005913719,0.00009483963,0.0002604937,0.0001481707,0.00002852946],"category_scores_gemma":[0.000004739781,0.0003094264,0.00008034825,0.0006425076,0.00009374376,0.0004938162,0.000002274934,0.0006245108,0.000008440136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002690997,"about_ca_system_score_gemma":0.00003511435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001579177,"about_ca_topic_score_gemma":0.0000277292,"domain_scores_codex":[0.9985882,0.00003569986,0.0004545895,0.0002440152,0.0002498786,0.0004275878],"domain_scores_gemma":[0.9991642,0.0002342932,0.0001085623,0.0002615711,0.0001225129,0.0001088081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002335419,0.00004246108,0.00001926244,0.00009527656,0.00001605414,0.00000397517,0.0005529963,0.5292537,0.0964941,0.000004364108,0.000003082234,0.3734913],"study_design_scores_gemma":[0.0001568009,0.00001953999,0.00001175973,0.000351761,0.00002379381,0.0000494902,0.000706586,0.4789029,0.5193431,0.0000385278,0.0001066366,0.0002891045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1043267,0.0001889881,0.8929319,0.000009512593,0.0001506452,0.0001658532,0.000003398631,0.001757973,0.0004650182],"genre_scores_gemma":[0.9689675,0.00001779199,0.03073527,0.00004822241,0.00005491135,0.00001561421,0.000001811322,0.0001046338,0.00005426442],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8646407,"threshold_uncertainty_score":0.9999358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03735929837253797,"score_gpt":0.3003324874686816,"score_spread":0.2629731890961436,"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."}}