{"id":"W4315630017","doi":"10.1109/globecom48099.2022.10000989","title":"An NWDAF Approach to 5G Core Network Signaling Traffic: Analysis and Characterization","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Flexibility (engineering); Cluster analysis; Core (optical fiber); Distributed computing; Core network; Cellular network; Artificial intelligence; Machine learning; Data modeling; Wireless network; Data science; Wireless; Computer network; Software engineering","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","sts"],"consensus_categories":[],"category_scores_codex":[0.001107285,0.0003244017,0.000465237,0.0002628383,0.002316768,0.0006104589,0.005078244,0.00007548807,0.00004274988],"category_scores_gemma":[0.00002673464,0.0003910316,0.0001456217,0.005433258,0.0001082439,0.0005302486,0.00235949,0.000541845,0.00001587178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003032989,"about_ca_system_score_gemma":0.0002336865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001647677,"about_ca_topic_score_gemma":0.00007280587,"domain_scores_codex":[0.9965516,0.0007681905,0.0005968434,0.00088631,0.0005748416,0.0006221531],"domain_scores_gemma":[0.9959965,0.0001259008,0.0003054587,0.003043247,0.0002154243,0.0003134861],"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":[0.0001497455,0.002955193,0.03795761,0.00008808281,0.001582672,0.00002763728,0.01440824,0.4233758,0.008716956,0.1335405,0.01489573,0.3623018],"study_design_scores_gemma":[0.0002338338,0.0001507224,0.01341278,0.00001013732,0.0001367893,0.00003090613,0.000333561,0.972164,0.00001564676,0.0007493786,0.0122261,0.000536197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.409473,0.000393558,0.5814465,0.001356178,0.002418316,0.0007234777,0.00009641462,0.0005628387,0.003529814],"genre_scores_gemma":[0.954497,0.00006694654,0.04319864,0.0009024506,0.0002579321,0.0001900103,0.0007965752,0.00001551732,0.00007496779],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5487881,"threshold_uncertainty_score":0.9998541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0488375982024802,"score_gpt":0.2870769206737382,"score_spread":0.238239322471258,"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."}}