{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006365788,0.0009024431,0.0003531459,0.0008100214,0.0002487727,0.0006131636,0.00113796,0.0003779796,0.003884794],"category_scores_gemma":[0.003020346,0.000231523,0.0004453689,0.0006798221,0.000364097,0.0006880757,0.0006494382,0.0006071262,0.0009741241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008548435,"about_ca_system_score_gemma":0.0009155993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01185964,"about_ca_topic_score_gemma":0.0114665,"domain_scores_codex":[0.9994654,0.00009121174,0.00002802854,0.0001291264,0.0002102049,0.00007610764],"domain_scores_gemma":[0.998895,0.0002748212,0.00006538467,0.0004320045,0.0002758545,0.00005691732],"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":[0.0009664496,0.0003214748,0.02383164,0.0002565605,0.0001782323,0.00160636,0.0003534561,0.6349376,0.02525409,0.02001174,0.04592749,0.246355],"study_design_scores_gemma":[0.00004216758,0.00006592852,0.002452745,0.00001054973,0.00001448003,0.0002098552,0.00003675667,0.9696183,0.01291966,0.004172519,0.01043447,0.00002263511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2894197,0.0002749728,0.5956353,0.0006062213,0.0004157389,0.0009800259,0.01337298,0.0809778,0.01831727],"genre_scores_gemma":[0.8453627,0.0001602719,0.1322606,0.0002125296,0.00004529748,0.0003652693,0.01391872,0.001652793,0.00602168],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01185964,"threshold_uncertainty_score":0.02358127,"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."}}