{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004416349,0.0002245952,0.0002724592,0.0001803754,0.00005750691,0.0002808895,0.0006285731,0.0003849889,0.00001772063],"category_scores_gemma":[0.00003393496,0.0002076632,0.00006076499,0.0003909654,0.00003106294,0.0001463185,0.00149154,0.000668748,0.00003526373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004855754,"about_ca_system_score_gemma":0.00005544879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005341545,"about_ca_topic_score_gemma":0.0002992804,"domain_scores_codex":[0.9982781,0.00005909301,0.0003252226,0.0007873723,0.0002059422,0.0003442858],"domain_scores_gemma":[0.998876,0.0001929061,0.00008950744,0.0007082706,0.00003702583,0.00009630131],"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":[0.0000197819,0.0001094816,0.09864704,0.0001566889,0.00009197661,0.0001351466,0.0007673117,0.4830895,0.00000231964,0.02812546,0.1238151,0.2650402],"study_design_scores_gemma":[0.0002017443,0.00003038758,0.1004153,0.000158657,0.000005889588,0.000006495578,0.000006858269,0.8815435,0.00000227004,0.01590102,0.001499037,0.0002288216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005523826,0.0008756556,0.9848245,0.001025265,0.003600417,0.000349173,0.0000053534,0.001283983,0.002511876],"genre_scores_gemma":[0.9546602,0.002621355,0.03276223,0.001046009,0.001597882,0.0002502481,0.00009428298,0.00008320335,0.00688462],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9520622,"threshold_uncertainty_score":0.8468259,"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."}}