{"id":"W4387385765","doi":"10.1109/icjece.2023.3275975","title":"Unsupervised Anomaly Detection for Rural Fixed Wireless LTE Networks","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Mitacs","keywords":"Anomaly detection; Computer science; DBSCAN; Cluster analysis; Wireless; Benchmark (surveying); Wireless network; Data mining; Real-time computing; Computer network; Artificial intelligence; Telecommunications; Geography; Cartography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0005010776,0.0004361069,0.0004334228,0.001204781,0.000409719,0.0005228785,0.0008127975,0.0003738443,0.0002779212],"category_scores_gemma":[0.00281918,0.0001614023,0.000270645,0.0008214994,0.0003617644,0.000688509,0.0005364916,0.0005687681,0.0001238964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006261154,"about_ca_system_score_gemma":0.0005182034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005709165,"about_ca_topic_score_gemma":0.006670426,"domain_scores_codex":[0.9995089,0.00009028981,0.00003163286,0.0001094676,0.0001887587,0.00007103507],"domain_scores_gemma":[0.9986948,0.000569124,0.0002163924,0.0001307215,0.000340943,0.00004789916],"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":[0.0003131418,0.0001164919,0.02687074,0.0000738073,0.0001136192,0.0003140326,0.0001851492,0.5158924,0.02520539,0.007616694,0.002484534,0.420814],"study_design_scores_gemma":[0.000002995111,0.00002397343,0.002240403,0.000001773964,0.000005361677,0.00008559413,0.00002645707,0.9918779,0.002610859,0.002578171,0.0005408124,0.000005671869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1163234,0.0002658219,0.8811607,0.0001330478,0.00003959108,0.00002610465,0.0001278659,0.001050959,0.0008725199],"genre_scores_gemma":[0.9129209,0.0001091088,0.08575176,0.00003724034,0.00004269822,0.0000196718,0.0002657312,0.00004089197,0.000812116],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005709165,"threshold_uncertainty_score":0.01135188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005940728105808942,"score_gpt":0.1812103187298789,"score_spread":0.1752695906240699,"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."}}