{"id":"W4401357210","doi":"10.1109/mwc.013.2300441","title":"Digital Twin Assisted Intelligent Network Management for Vehicular Applications","year":2024,"lang":"en","type":"article","venue":"IEEE Wireless Communications","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Network management; Cloud computing; Distributed computing; Benchmark (surveying); Edge computing; Server; Process (computing); Adaptation (eye); Network management station; Artificial intelligence; Computer network; Enhanced Data Rates for GSM Evolution; Network architecture; Operating system","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002412997,0.0001773559,0.0001707001,0.0001085419,0.0004722363,0.0007764206,0.002660494,0.0000771542,0.000003532849],"category_scores_gemma":[0.000005726419,0.0001753634,0.0001754543,0.001001137,0.00009688035,0.0003584712,0.0006006459,0.0002105948,0.0001449267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009723436,"about_ca_system_score_gemma":0.00004926194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003306861,"about_ca_topic_score_gemma":0.0000128258,"domain_scores_codex":[0.9986537,0.00004683997,0.0003620832,0.0004128645,0.0001838986,0.000340667],"domain_scores_gemma":[0.9963959,0.0005908835,0.00006525913,0.002744853,0.00009766267,0.0001053987],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001437955,0.0001144649,0.00007576098,0.00003999238,0.0001119363,0.000002212715,0.00008250104,0.002534962,0.000009477951,0.4439909,0.009362171,0.5436742],"study_design_scores_gemma":[0.0001023877,0.00002121595,0.0002893211,0.0001245695,0.00003545385,0.000009440939,0.0000234829,0.3258218,0.00002762339,0.01252075,0.6607966,0.0002272991],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001816314,0.003542227,0.9869127,0.002385946,0.0004331666,0.0008927385,0.0000216067,0.0008487522,0.004781284],"genre_scores_gemma":[0.8212167,0.001689546,0.1701478,0.0004570185,0.0003531609,0.003872418,0.0002241756,0.00005939202,0.001979768],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8210351,"threshold_uncertainty_score":0.7487041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03403768303608094,"score_gpt":0.2852810355395939,"score_spread":0.251243352503513,"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."}}