{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004517419,0.0003770593,0.0004240372,0.0001323103,0.0003800239,0.0002555089,0.0013127,0.000369524,0.000007666337],"category_scores_gemma":[0.0001508846,0.0004409581,0.0001843315,0.00103249,0.0001248291,0.000259617,0.002104645,0.0003974449,0.00004005174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001101386,"about_ca_system_score_gemma":0.0001409791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000764098,"about_ca_topic_score_gemma":0.00009525827,"domain_scores_codex":[0.9975176,0.0001209209,0.0002870096,0.001348903,0.00009731715,0.0006281923],"domain_scores_gemma":[0.9973459,0.0009834875,0.0002965413,0.0009507971,0.0002260198,0.0001972846],"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":[0.00001995237,0.00001370527,0.002100344,0.00005720704,0.00005211369,0.00004302424,0.0001086313,0.6946318,1.218536e-7,0.2975951,0.003847967,0.001530122],"study_design_scores_gemma":[0.0001394928,0.00007054424,0.001145002,0.0001830071,0.0000680162,0.000002704579,0.00003055524,0.6805772,0.000001416828,0.3162906,0.001115261,0.0003761663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004841421,0.0004162567,0.9909887,0.0001157478,0.002308107,0.0005942916,0.00001901821,0.0005763101,0.0001401078],"genre_scores_gemma":[0.9827296,0.001797418,0.01221658,0.0003504838,0.001148269,0.00001118865,0.00009032051,0.00005986558,0.001596316],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9787722,"threshold_uncertainty_score":0.9998042,"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."}}