{"id":"W3005412203","doi":"10.1109/tit.2020.2971474","title":"Generalized Gaussian Multiterminal Source Coding: The Symmetric Case","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"National Natural Science Foundation of China","keywords":"Distortion (music); Gaussian; Encoder; Coding (social sciences); Distributed source coding; Rate–distortion theory; Source code; Algorithm; Mathematics; Decoding methods; Discrete mathematics; Rate distortion; Computer science; Variable-length code; Theoretical computer science; Topology (electrical circuits); Data compression; Combinatorics; Statistics; Telecommunications; Physics","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":[0.0002530504,0.0001477687,0.0001199771,0.0002060431,0.0002765822,0.0001020671,0.0002866187,0.00008477243,0.0001635845],"category_scores_gemma":[0.00001479622,0.0001239259,0.00008986297,0.0004909119,0.0000626025,0.0006676539,0.00000228214,0.0003900618,0.0002215112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000059151,"about_ca_system_score_gemma":0.00001249537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001094541,"about_ca_topic_score_gemma":0.000003211952,"domain_scores_codex":[0.9991688,0.000105901,0.0003497072,0.00006587068,0.0001609823,0.0001487788],"domain_scores_gemma":[0.9992358,0.0001938358,0.00006180637,0.0003667297,0.00005090835,0.00009091869],"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":[0.000178438,0.00008187506,0.00000312846,0.0002815738,0.0002006299,0.00002607717,0.03888595,0.3326633,0.001040607,0.05233048,0.003421561,0.5708864],"study_design_scores_gemma":[0.0008994678,0.00007346545,0.00001213536,0.00003950028,0.00006768182,0.0004475151,0.002695801,0.8819388,0.05456437,0.0005210232,0.05826219,0.000478061],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01353953,0.00003712352,0.9811163,0.0005211342,0.000140592,0.000276479,0.0000354388,0.001109452,0.003223948],"genre_scores_gemma":[0.9971263,0.00009958819,0.001451182,0.001137107,0.00002693365,0.00009672633,0.00000777401,0.00001995813,0.00003439033],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9835868,"threshold_uncertainty_score":0.5053552,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01766551202335206,"score_gpt":0.2336191423622176,"score_spread":0.2159536303388656,"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."}}