{"id":"W3046822646","doi":"10.1109/mnet.011.2000373","title":"The Need for Advanced Intelligence in NFV Management and Orchestration","year":2020,"lang":"en","type":"preprint","venue":"IEEE Network","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Orchestration; Computer science; Leverage (statistics); Scalability; Network Functions Virtualization; Transferability; Reinforcement learning; Virtualization; Business intelligence; Scope (computer science); Distributed computing; Cloud computing; Artificial intelligence; Knowledge management; Database; Machine learning","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":[0.0004418088,0.000231049,0.0002389876,0.00003584219,0.0001733632,0.0003366766,0.0009458739,0.0001442661,3.421118e-7],"category_scores_gemma":[0.00002576452,0.0001887487,0.00007200429,0.000355188,0.00004642158,0.00009690427,0.0006088628,0.0003897031,0.00000430288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004719296,"about_ca_system_score_gemma":0.00004414567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009174577,"about_ca_topic_score_gemma":0.00005953281,"domain_scores_codex":[0.998355,0.00006569154,0.000363496,0.0006397931,0.0001792251,0.0003967542],"domain_scores_gemma":[0.9986134,0.0005174836,0.0001661546,0.0005853478,0.00004100878,0.00007658915],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001151284,0.00002244928,0.000258993,0.0001510643,0.00005569924,0.00001719478,0.0003848914,0.4895866,0.000002464013,0.05201463,0.01942627,0.4379646],"study_design_scores_gemma":[0.0003191578,0.0001129461,0.001986658,0.0003724869,0.00002184494,0.000002521527,0.0000390903,0.4526868,0.00003487509,0.516979,0.02699509,0.0004495448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007387873,0.002664387,0.9869031,0.002187335,0.00563397,0.001175009,0.000001911318,0.0001627155,0.0005327789],"genre_scores_gemma":[0.6199065,0.008690671,0.3586356,0.002705634,0.007388027,0.001988703,0.0000432345,0.00009150444,0.0005501502],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6282675,"threshold_uncertainty_score":0.7696949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0312132202388375,"score_gpt":0.2707375263730373,"score_spread":0.2395243061341998,"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."}}