{"id":"W4385269915","doi":"10.1109/eucnc/6gsummit58263.2023.10188363","title":"ML KPI Prediction in 5G and B5G Networks","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ciena (Canada); Concordia University","funders":"Mitacs","keywords":"Performance indicator; Computer science; Context (archaeology); Metric (unit); Throughput; Slicing; Performance metric; Quality of service; Service (business); Network performance; End-to-end principle; Key (lock); Distributed computing; Computer network; Telecommunications; Wireless; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001462515,0.001087357,0.0009318654,0.0008628828,0.0004170189,0.001290722,0.001006647,0.001150035,0.001062164],"category_scores_gemma":[0.005033338,0.0004600697,0.0004948772,0.0009537212,0.0006807379,0.001606657,0.0007255864,0.001472274,0.000250418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002021991,"about_ca_system_score_gemma":0.0009769822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02293096,"about_ca_topic_score_gemma":0.01223931,"domain_scores_codex":[0.9992595,0.0002356867,0.00003648889,0.0001881257,0.0001385273,0.0001416233],"domain_scores_gemma":[0.9977502,0.001484774,0.0002746097,0.00009416788,0.0003065699,0.0000897283],"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":[0.00003409755,0.00001470119,0.001079264,0.00001155017,0.000009603924,0.0000257354,0.00001150528,0.9915353,0.000166359,0.0009830522,0.0002142948,0.005914589],"study_design_scores_gemma":[8.226271e-7,0.000003102023,0.0001132192,0.000001419703,0.000001188854,0.000002558553,0.000002560225,0.9992301,0.00006942215,0.0005409844,0.00003320831,0.000001475392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.222866,0.0007506433,0.7703165,0.001055735,0.00008605786,0.00009129614,0.0005359459,0.0009850834,0.003312662],"genre_scores_gemma":[0.9797849,0.000161321,0.0187924,0.00007951749,0.00002871773,0.00003954824,0.0002365644,0.00002529799,0.0008516561],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02293096,"threshold_uncertainty_score":0.04559493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0294563790145979,"score_gpt":0.2391747178744387,"score_spread":0.2097183388598408,"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."}}