{"id":"W3035528052","doi":"10.1109/tccn.2020.2993976","title":"IEEE TCCN Special Section Editorial: Intelligent Resource Management for 5G and Beyond","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Age of Information Optimization","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Engineering and Physical Sciences Research Council; National Natural Science Foundation of China","keywords":"Computer science; Special section; Resource management (computing); Resource (disambiguation); Process (computing); Section (typography); Cognition; Data science; Telecommunications; Computer network; Medicine","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":[0.0002326549,0.0001480342,0.0001335139,0.0001232457,0.0008446517,0.0002238337,0.0003537651,0.00008088409,0.000005590799],"category_scores_gemma":[0.000005233006,0.000163963,0.00005193201,0.0003734833,0.00009720471,0.0005559797,0.00001528419,0.0002458184,0.000006030543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004054083,"about_ca_system_score_gemma":0.00001699831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002686668,"about_ca_topic_score_gemma":0.0000111977,"domain_scores_codex":[0.9990147,0.00008844957,0.000294998,0.0002642625,0.0001682889,0.0001692727],"domain_scores_gemma":[0.9988495,0.0004087872,0.0001288067,0.0003297365,0.0001750291,0.000108164],"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":[0.000136243,0.0001201339,0.000005357222,0.00006271966,0.0001323193,7.061893e-7,0.004590147,0.006559329,0.00002312451,0.002939391,0.008534698,0.9768958],"study_design_scores_gemma":[0.0014396,0.0004436319,0.00001919116,0.0002117326,0.0001164869,0.000009520325,0.001626275,0.6387408,0.001069744,0.0004899802,0.3553753,0.0004578075],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00006337681,0.000165752,0.9843425,0.001529978,0.00517716,0.0005999216,0.00001221966,0.000126302,0.007982749],"genre_scores_gemma":[0.9021133,0.01239913,0.06243569,0.00321418,0.019074,0.0004934344,0.00005569523,0.00004937174,0.000165159],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.976438,"threshold_uncertainty_score":0.6686218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03674078025100141,"score_gpt":0.2644072878818417,"score_spread":0.2276665076308403,"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."}}