{"id":"W4310815533","doi":"10.48550/arxiv.2107.04748","title":"Resilient Edge Service Placement under Demand and Node Failure Uncertainties","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Age of Information Optimization","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Workload; Mathematical optimization; Distributed computing; Node (physics); Resource allocation; Enhanced Data Rates for GSM Evolution; Service (business); Set (abstract data type); Resource (disambiguation); Edge computing; Operations research; Computer network; Engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001802785,0.000258753,0.0002271528,0.000202611,0.0001987422,0.0003668326,0.0008211478,0.0002221564,0.00005865364],"category_scores_gemma":[0.00001673008,0.0002997033,0.00006734922,0.0004798742,0.00005738156,0.0008829851,0.002386474,0.0003084362,0.00003727719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002361527,"about_ca_system_score_gemma":0.0002390822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007934345,"about_ca_topic_score_gemma":0.0001611715,"domain_scores_codex":[0.9986443,0.00009540328,0.0002147004,0.0006740609,0.000129811,0.0002417467],"domain_scores_gemma":[0.9983739,0.00005654397,0.0002427763,0.0008286262,0.0003677438,0.0001304109],"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":[0.000009767226,0.0000241843,0.0003575926,0.0001225336,0.00005182149,0.00003228382,0.001115388,0.9636542,0.000006533656,0.0340001,0.0005701677,0.00005540062],"study_design_scores_gemma":[0.0004839543,0.00001854079,0.0007448853,0.0001437635,0.00004025819,0.000006831211,0.002041438,0.9933406,0.0001255568,0.002258695,0.0004088079,0.0003867199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1335678,0.00007367755,0.8631868,0.0008428592,0.0002551631,0.0002241642,0.000004692078,0.0001234548,0.00172138],"genre_scores_gemma":[0.9826447,0.000214718,0.01503117,0.0007549312,0.00002937819,0.000001348545,0.00005216197,0.00001169411,0.00125991],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8490769,"threshold_uncertainty_score":0.9999455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03616013504032411,"score_gpt":0.1754315684299849,"score_spread":0.1392714333896608,"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."}}