{"id":"W4297802410","doi":"10.1109/iccworkshops53468.2022.9882152","title":"Machine Learning Based Dynamic Restoration of Next Generation Wireless Networks","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"","keywords":"Computer science; Unavailability; Computer network; Wireless network; Downtime; Wireless; Distributed computing; Service (business); Context (archaeology); Telecommunications; Reliability engineering; Engineering","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":[0.001209582,0.0003918252,0.0004105854,0.0005875093,0.001290464,0.000469231,0.005266055,0.0001730971,0.000665414],"category_scores_gemma":[0.0001457488,0.0004507464,0.0002277591,0.001711566,0.0001879404,0.0007055413,0.001311432,0.001769928,0.00002316116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004889865,"about_ca_system_score_gemma":0.0002793169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007312318,"about_ca_topic_score_gemma":0.0002435573,"domain_scores_codex":[0.9956873,0.001054781,0.0009461893,0.0007532517,0.001121367,0.0004371008],"domain_scores_gemma":[0.9950597,0.0009010706,0.000793609,0.002559932,0.0005469296,0.0001387425],"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":[0.0001093103,0.0005953209,0.0006347153,0.000006672623,0.0001102842,0.000008180333,0.0004299468,0.7649097,0.001188577,0.1300111,0.00277448,0.09922175],"study_design_scores_gemma":[0.0005995026,0.0001622217,0.0002737125,0.00008241493,0.00002166378,0.000011837,0.0002206467,0.9906323,0.00005731044,0.0008713036,0.006654937,0.0004121271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007099127,0.001379754,0.969667,0.01415234,0.003037092,0.0006552828,0.00005816931,0.0004933624,0.003457887],"genre_scores_gemma":[0.9837781,0.001500725,0.01115099,0.0008410157,0.0001582703,0.0004746853,0.001193367,0.00005101328,0.0008518632],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.976679,"threshold_uncertainty_score":0.9997944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0977551964294902,"score_gpt":0.3102387438654169,"score_spread":0.2124835474359267,"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."}}