{"id":"W2976956053","doi":"10.1109/isit.2019.8849577","title":"Joint Source-Channel Coding for the Transmission of Correlated Sources over Two-Way Channels","year":2019,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Lossy compression; Channel code; Lossless compression; Coding (social sciences); Computer science; Variable-length code; Tunstall coding; Algorithm; Channel (broadcasting); Decoding methods; Source code; Theoretical computer science; Context-adaptive binary arithmetic coding; Shannon–Fano coding; Adaptive coding; Context-adaptive variable-length coding; Mathematics; Coding tree unit; Telecommunications; Data compression; Statistics; Artificial intelligence","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":[0.0002880168,0.0001369868,0.0002114559,0.00008443832,0.00006518896,0.00002475766,0.0003173136,0.00009439632,0.000223942],"category_scores_gemma":[0.0000127283,0.0000988867,0.0001177012,0.0001291064,0.00003413174,0.00009504214,0.00004708838,0.0001740788,0.00001325819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002441836,"about_ca_system_score_gemma":0.000005935132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004782059,"about_ca_topic_score_gemma":0.000003220342,"domain_scores_codex":[0.999247,0.00002396586,0.0002969256,0.0001131608,0.000138333,0.0001806322],"domain_scores_gemma":[0.9991085,0.0002900403,0.00005889484,0.0004469819,0.00005738163,0.00003818745],"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":[0.0001139606,0.0001705065,0.0003076532,0.0009505223,0.0004487345,4.77639e-7,0.02917108,0.484268,0.4140653,0.01848478,0.0112489,0.04077014],"study_design_scores_gemma":[0.0004321128,0.00003487812,0.00008136709,0.0001199097,0.00001632656,0.000001589258,0.0004284962,0.7947279,0.1907124,0.0004277217,0.01286648,0.0001507978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2740991,0.0008724602,0.7193375,0.0002866618,0.0002231638,0.0009294171,0.000006992585,0.0008950988,0.003349574],"genre_scores_gemma":[0.9977405,0.0002348407,0.001296187,0.00004854588,0.00002517777,0.00004886541,0.000007459975,0.00004411547,0.0005542791],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7236414,"threshold_uncertainty_score":0.4032483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02007848606354787,"score_gpt":0.2483211389639849,"score_spread":0.228242652900437,"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."}}