{"id":"W2940299710","doi":"10.1109/tmc.2019.2911935","title":"<i>Razor</i>: Scaling Backend Capacity for Mobile Applications","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Wuhan University; National Natural Science Foundation of China","keywords":"Computer science; Burstiness; Schedule; Mobile device; Computer network; Real-time computing; Key (lock); Distributed computing; Term (time); Operating system; Network packet","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001793889,0.002407594,0.000758241,0.001321465,0.0007987699,0.001824573,0.005741793,0.001209748,0.004565156],"category_scores_gemma":[0.0057837,0.0007095284,0.0006585788,0.000954377,0.001042319,0.003473622,0.00244917,0.001658112,0.001996625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001392111,"about_ca_system_score_gemma":0.001406622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006512863,"about_ca_topic_score_gemma":0.007100706,"domain_scores_codex":[0.998525,0.0002653804,0.0001292289,0.0004019759,0.0004073214,0.0002710643],"domain_scores_gemma":[0.9965143,0.0006741302,0.0003988748,0.001171233,0.0008599021,0.0003815445],"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":[0.002517645,0.0007583281,0.01275772,0.0007881041,0.0002784141,0.0005436189,0.0005800519,0.1569558,0.1163311,0.01360399,0.07159352,0.6232917],"study_design_scores_gemma":[0.0001588785,0.0008146133,0.002377449,0.00007405974,0.00008885355,0.0003444944,0.0001113883,0.9108654,0.05741106,0.005588455,0.02198642,0.0001788504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1046346,0.002979437,0.7790868,0.001950013,0.0006509448,0.0007842093,0.001188379,0.0964841,0.01224138],"genre_scores_gemma":[0.714573,0.0008564099,0.2737753,0.001437367,0.0002512606,0.000312403,0.001263731,0.001979355,0.005551048],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006512863,"threshold_uncertainty_score":0.01527196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01674455067961437,"score_gpt":0.2432229926257437,"score_spread":0.2264784419461294,"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."}}