{"id":"W4386280474","doi":"10.1109/mobilecloud58788.2023.00013","title":"A Fault Detection Mechanism for Database Management Systems on Mobile Edge Computing","year":2023,"lang":"en","type":"article","venue":"","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Testbed; Generality; Database; Enhanced Data Rates for GSM Evolution; Anomaly detection; Context (archaeology); Edge computing; Data mining; Distributed computing; Computer network; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003327235,0.0007858959,0.0010395,0.002577058,0.001090651,0.00254497,0.002609136,0.001481171,0.0009113377],"category_scores_gemma":[0.01055302,0.0004137643,0.0004909886,0.001396422,0.0005744398,0.002662181,0.001273864,0.001156504,0.0007123621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008650648,"about_ca_system_score_gemma":0.001126749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001876126,"about_ca_topic_score_gemma":0.00122175,"domain_scores_codex":[0.997323,0.0003573223,0.0004410898,0.0006694779,0.0009829259,0.0002261399],"domain_scores_gemma":[0.9922943,0.001536582,0.0009385968,0.002884031,0.002061562,0.0002849059],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002326718,0.0008671245,0.03393853,0.0005417495,0.0003305366,0.0008881522,0.0004981933,0.05491231,0.07529873,0.01694598,0.01554774,0.7979042],"study_design_scores_gemma":[0.0001437857,0.0007364723,0.009490222,0.00008141807,0.0001682094,0.001395633,0.0001721912,0.8809299,0.0832683,0.01054428,0.01297229,0.00009729601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09991832,0.001412237,0.8723344,0.0005116135,0.0002254568,0.0006299177,0.0006347271,0.02265454,0.001678763],"genre_scores_gemma":[0.7821289,0.0002738764,0.2153642,0.0002644016,0.00008354167,0.0001916646,0.0006491676,0.0001090436,0.0009352838],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003327235,"threshold_uncertainty_score":0.0175963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03022197900581269,"score_gpt":0.2731556284453712,"score_spread":0.2429336494395585,"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."}}