{"id":"W2917753748","doi":"10.3390/e21020213","title":"Asymptotic Rate-Distortion Analysis of Symmetric Remote Gaussian Source Coding: Centralized Encoding vs. Distributed Encoding","year":2019,"lang":"en","type":"article","venue":"Entropy","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Gaussian; Independent and identically distributed random variables; Encoding (memory); Distortion (music); Distributed source coding; Algorithm; Computer science; Rate distortion; Compressed sensing; Coding (social sciences); Source code; Decoding methods; Rate–distortion theory; Mathematics; Data compression; Random variable; Variable-length code; Statistics; Artificial intelligence; Telecommunications; Bandwidth (computing); 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003918475,0.0002289202,0.0005790741,0.000856897,0.00007074855,0.00005542428,0.0004473553,0.0001360568,0.0002462958],"category_scores_gemma":[0.0001444569,0.0002510327,0.0002585117,0.002408898,0.00004010819,0.0001888696,0.00008387738,0.0002675849,0.00003687818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003864834,"about_ca_system_score_gemma":0.00001637827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009222625,"about_ca_topic_score_gemma":0.00001050246,"domain_scores_codex":[0.998331,0.0001499396,0.0006001069,0.0002554245,0.0002945148,0.0003690562],"domain_scores_gemma":[0.9985783,0.0002225996,0.0002197042,0.0007866803,0.00008258728,0.0001101157],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002256698,0.0003312271,0.1135647,0.000898898,0.004749005,0.00001738913,0.00476168,0.1720445,0.6079887,0.08602295,0.001608331,0.00778706],"study_design_scores_gemma":[0.0008448266,0.00005821427,0.02479221,0.0001776292,0.0007350756,0.000002804989,0.0001645678,0.8740688,0.09144436,0.0002710221,0.006867307,0.0005731997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7299923,0.0003607266,0.2666037,0.00009690768,0.0002261417,0.0003674598,0.00004682704,0.0009248556,0.001381017],"genre_scores_gemma":[0.9969977,0.0004285099,0.002187959,0.00001826397,0.00002470362,0.000006018209,0.0002475645,0.000040468,0.00004879141],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7020243,"threshold_uncertainty_score":0.9999942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01053967525680791,"score_gpt":0.2364137313715194,"score_spread":0.2258740561147115,"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."}}