{"id":"W4296154953","doi":"10.48550/arxiv.2209.06852","title":"A Model Drift Detection and Adaptation Framework for 5G Core Networks","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Orchestration; Computer science; Adaptation (eye); Core network; Concept drift; Core (optical fiber); Automation; Distributed computing; Focus (optics); Artificial intelligence; Real-time computing; Computer network; Telecommunications; Engineering; Data stream","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002740869,0.0002336937,0.0002215393,0.0002154041,0.0002620149,0.0001305363,0.001182173,0.0003080798,0.000005123951],"category_scores_gemma":[0.00007093848,0.0003063599,0.0001036059,0.0003572818,0.00006017199,0.0003506275,0.001384262,0.0006327166,0.000001026905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001702935,"about_ca_system_score_gemma":0.0000974158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007626037,"about_ca_topic_score_gemma":0.00004915502,"domain_scores_codex":[0.9984541,0.00005680121,0.0001631374,0.001004281,0.0000703778,0.0002513383],"domain_scores_gemma":[0.9983449,0.0001997216,0.0002730625,0.001003257,0.00008822927,0.00009082046],"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":[0.00003409724,0.00002623103,0.0001744203,0.00003330914,0.00002953009,0.00001630617,0.000246105,0.721646,0.00000597118,0.2696742,0.0001579715,0.007955934],"study_design_scores_gemma":[0.0001055228,0.0000704646,0.00005953311,0.00004054516,0.00003046157,0.000001824522,0.00004014968,0.7212006,0.00001802778,0.2780813,0.0001357989,0.0002157754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01808818,0.00003916866,0.9801368,0.00003253732,0.0003320719,0.0004905271,0.00003961567,0.0005907813,0.000250392],"genre_scores_gemma":[0.7997294,0.0001144068,0.1998489,0.0000617938,0.00004014796,0.00001271283,0.00004310584,0.00001785226,0.0001316964],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7816412,"threshold_uncertainty_score":0.9999388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1468047088944169,"score_gpt":0.2249937184551097,"score_spread":0.07818900956069288,"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."}}