{"id":"W4313484200","doi":"10.48550/arxiv.2301.00519","title":"Holistic Network Virtualization and Pervasive Network Intelligence for 6G","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Virtualization; Network virtualization; Network architecture; Distributed computing; Service (business); Computer network; Architecture; Orchestration; Cloud computing; Operating system","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005868079,0.0005054541,0.0002894651,0.0003322709,0.0006454305,0.002602965,0.0006154173,0.0008578954,0.00214858],"category_scores_gemma":[0.0006858697,0.00020999,0.0004220536,0.0003764075,0.001335407,0.004565535,0.001523237,0.001750462,0.000347448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001030777,"about_ca_system_score_gemma":0.0008329985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009380383,"about_ca_topic_score_gemma":0.001193121,"domain_scores_codex":[0.9996713,0.0001145863,0.00001236835,0.00005751994,0.00009347365,0.00005071723],"domain_scores_gemma":[0.9998097,0.00005897386,0.00001649919,0.00003902125,0.00004260288,0.00003337496],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009721924,0.000005942491,0.0001855901,0.00005495321,0.000009041535,0.00004392154,0.0001156565,0.005567726,0.001072528,0.9667613,0.002331035,0.02384255],"study_design_scores_gemma":[0.000005865881,0.0000599158,0.0004998365,0.0001404601,0.00003220517,0.0003466287,0.0002600607,0.08160313,0.001750399,0.7417747,0.1734979,0.00002881588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02415627,0.01409005,0.8700249,0.01133125,0.001766622,0.00007488646,0.0000858906,0.0005350953,0.07793501],"genre_scores_gemma":[0.7084481,0.02625793,0.2397311,0.002182759,0.001609929,0.0001519728,0.0002151953,0.0001291883,0.02127385],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002602965,"threshold_uncertainty_score":0.007478833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1722728202422833,"score_gpt":0.2237675596511653,"score_spread":0.05149473940888205,"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."}}