{"id":"W2829760985","doi":"10.1109/infcomw.2018.8406965","title":"Fair multi-resource allocation with external resource for mobile edge computing","year":2018,"lang":"en","type":"article","venue":"","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Server; Resource allocation; Mobile edge computing; Upload; Distributed computing; Computer network; Resource management (computing); Resource (disambiguation); Wireless; Enhanced Data Rates for GSM Evolution; Operating system; Telecommunications","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":[0.0004583541,0.0001956114,0.0001794669,0.0001062057,0.0004847926,0.0002562967,0.0008871516,0.00005652382,0.000002265232],"category_scores_gemma":[0.00002731978,0.000157175,0.000065896,0.0003114406,0.00009392186,0.0002524112,0.0003499446,0.0001143304,0.00005869812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004912605,"about_ca_system_score_gemma":0.00005357912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003008917,"about_ca_topic_score_gemma":0.000006075346,"domain_scores_codex":[0.9984327,0.00004809295,0.0002370518,0.0005508278,0.000237776,0.0004935446],"domain_scores_gemma":[0.9988369,0.0001965807,0.000133576,0.00051927,0.0001957687,0.0001178664],"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":[0.0001296573,0.0005589354,0.00406634,0.000125492,0.00008674336,0.00001505353,0.02620947,0.003355806,0.005247222,0.007115695,0.05111802,0.9019716],"study_design_scores_gemma":[0.0008153848,0.0004829851,0.001034028,0.00007032343,0.000007009273,0.00003077118,0.0001178747,0.8864045,0.006604227,0.0001218605,0.1040154,0.0002957296],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1040511,0.00003356706,0.8909352,0.0001528459,0.0008909654,0.0003852246,1.291058e-7,0.0004016245,0.003149366],"genre_scores_gemma":[0.608018,1.821559e-7,0.3857313,0.0006191435,0.00394644,0.00002025497,0.000003491598,0.00002584824,0.001635262],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9016758,"threshold_uncertainty_score":0.640941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02093136816669036,"score_gpt":0.2668214914368823,"score_spread":0.245890123270192,"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."}}