{"id":"W3139063192","doi":"10.1145/3448613","title":"Spatio-temporal Bayesian Learning for Mobile Edge Computing Resource Planning in Smart Cities","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Internet Technology","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Edge computing; Cloud computing; Software deployment; Mobile edge computing; Smart city; Quality of service; Service (business); Server; Distributed computing; Resource (disambiguation); Computer network; Task (project management); Enhanced Data Rates for GSM Evolution; Internet of Things; Artificial intelligence; World Wide Web","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":[],"consensus_categories":[],"category_scores_codex":[0.0002725367,0.0002038257,0.0002868299,0.000672866,0.0002331636,0.0001202091,0.0009599234,0.0002408136,0.000009612657],"category_scores_gemma":[0.0001064762,0.000236718,0.000106288,0.00083738,0.00008360468,0.0001633869,0.0001111158,0.0007301022,0.00001530363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001116537,"about_ca_system_score_gemma":0.00007243505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004343784,"about_ca_topic_score_gemma":0.00004391962,"domain_scores_codex":[0.9983034,0.00007798986,0.0004067741,0.0005896452,0.0001269782,0.0004952563],"domain_scores_gemma":[0.9988006,0.0003705069,0.0001091125,0.0005873668,0.00008565356,0.00004672753],"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":[0.00009083325,0.0004143738,0.04007643,0.0001567495,0.0001526395,0.0002913979,0.01172734,0.0431704,0.0004917367,0.004250333,0.002960914,0.8962169],"study_design_scores_gemma":[0.001800342,0.001040997,0.0009589919,0.0007448894,0.00002374395,0.000335061,0.002719822,0.7450054,0.04255119,0.009399238,0.1945069,0.0009134462],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1672207,0.0001279778,0.8288471,0.001035542,0.00158541,0.0001760013,3.739631e-7,0.0006156793,0.0003912627],"genre_scores_gemma":[0.942031,0.000002673119,0.05692532,0.0001489282,0.0001434065,0.00004510498,0.00001192132,0.0000235994,0.0006679896],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8953034,"threshold_uncertainty_score":0.9653078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01768205073847018,"score_gpt":0.2650526188428091,"score_spread":0.2473705681043389,"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."}}