{"id":"W4389077385","doi":"10.23919/cnsm59352.2023.10327908","title":"5G E2E Network Slicing Predictable Traffic Generator","year":2023,"lang":"en","type":"article","venue":"","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Artificial Intelligence in Medicine (Canada); Ericsson (Canada)","funders":"","keywords":"Computer science; Slicing; Traffic generation model; Traffic classification; Code refactoring; Generator (circuit theory); Floating car data; Distributed computing; Network traffic simulation; Resource (disambiguation); Traffic shaping; Data modeling; Data mining; Machine learning; Network traffic control; Artificial intelligence; Real-time computing; Computer network; Database; Quality of service; Software; Engineering; Transport engineering; World Wide Web; Operating system; Traffic congestion","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003735363,0.0001443738,0.0001678966,0.0000728728,0.0002399029,0.0002123802,0.0006596596,0.00007756361,0.00006777912],"category_scores_gemma":[0.00002295265,0.0001224352,0.00006907515,0.001581057,0.00001778045,0.0002755628,0.0001993029,0.0001250018,0.0005351172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002136307,"about_ca_system_score_gemma":0.00005397499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001829199,"about_ca_topic_score_gemma":0.00002173606,"domain_scores_codex":[0.9984607,0.00003862564,0.0002130393,0.0004109853,0.0002537474,0.0006229363],"domain_scores_gemma":[0.9990862,0.0001604646,0.00004158001,0.000532212,0.00003907655,0.0001404368],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002722067,0.00002175825,0.002388203,0.000008511761,0.00002259237,0.0000271286,0.0002093554,0.3633639,0.000041592,0.018293,0.5678823,0.04773892],"study_design_scores_gemma":[0.0002502985,0.00007027441,0.002547967,0.00002314581,0.000005115062,0.000007710521,0.00001193544,0.9437708,0.00007960301,0.001004552,0.05200162,0.0002270301],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2092502,0.0009507744,0.7605019,0.002444018,0.004717191,0.0004220351,0.000003157106,0.008657027,0.01305367],"genre_scores_gemma":[0.9378088,0.0001673589,0.04740647,0.00302627,0.002501884,0.00005970128,0.0000166625,0.00004720348,0.008965623],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7285586,"threshold_uncertainty_score":0.6878027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01602834882149782,"score_gpt":0.2194871315391773,"score_spread":0.2034587827176795,"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."}}