{"id":"W2950831396","doi":"10.48550/arxiv.1602.02294","title":"A Source-Channel Separation Theorem with Application to the Source Broadcast Problem","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Converse; Separation (statistics); Source separation; Channel (broadcasting); Reduction (mathematics); Argument (complex analysis); Computer science; Mutual fund separation theorem; Mathematics; Algorithm; Telecommunications; Geometry; Economics","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":[0.0002597243,0.000339399,0.000251668,0.0001983269,0.0001773731,0.00008108183,0.001234884,0.0002657236,0.0000147784],"category_scores_gemma":[0.000008860336,0.0002709895,0.00009574487,0.0004011725,0.0001058193,0.0001513266,0.0006508629,0.0005317018,0.0001539473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002763679,"about_ca_system_score_gemma":0.00004487941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007038558,"about_ca_topic_score_gemma":0.0001274191,"domain_scores_codex":[0.9987638,0.0001162391,0.000203243,0.0005267069,0.0001076513,0.000282338],"domain_scores_gemma":[0.997762,0.00009356432,0.000145514,0.001716847,0.0001665103,0.0001156117],"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":[0.00005397349,0.00003652907,0.0001688506,0.00009055658,0.0001063817,0.000002104591,0.001710436,0.961275,0.0003468879,0.02943638,0.001357532,0.00541532],"study_design_scores_gemma":[0.0005597365,0.00009806303,0.000240226,0.0005374633,0.0001644161,0.00001258691,0.0005472981,0.8585339,0.003187625,0.03678982,0.09804858,0.001280273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05929181,0.00006733116,0.9320793,0.0003452168,0.00004132442,0.001165828,0.0000222285,0.001317349,0.005669634],"genre_scores_gemma":[0.9978781,0.0001674494,0.0006996572,0.00007171788,0.00008890816,0.00005132403,0.00004138143,0.00008033275,0.0009210891],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9385864,"threshold_uncertainty_score":0.9999743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02522740399910895,"score_gpt":0.1848184136433601,"score_spread":0.1595910096442512,"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."}}