{"id":"W2951800620","doi":"10.48550/arxiv.1707.09662","title":"Adaptive Delivery in Caching Networks","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Redundancy (engineering); Computer network; Upper and lower bounds; Transmission (telecommunications); Popularity; Transmission rate; Content delivery; Distributed computing; Real-time computing; Mathematics; Telecommunications; Operating system","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":[0.001664347,0.0007004727,0.0009841195,0.000867018,0.0007966859,0.001293281,0.001579275,0.001538565,0.001602665],"category_scores_gemma":[0.009462336,0.0005630882,0.0004764727,0.001403434,0.001114962,0.001846576,0.001065422,0.001284275,0.0002698553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002201117,"about_ca_system_score_gemma":0.0006865048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003459022,"about_ca_topic_score_gemma":0.001681407,"domain_scores_codex":[0.9989022,0.0004886631,0.00004593781,0.0001772939,0.0002572123,0.0001286129],"domain_scores_gemma":[0.9956402,0.003148462,0.0003216289,0.0003128715,0.0004762988,0.0001005382],"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":[0.0002992998,0.00005881418,0.0009994145,0.0002500605,0.0000703982,0.0002957846,0.0002028018,0.8454282,0.00858072,0.08159473,0.003680184,0.05853957],"study_design_scores_gemma":[0.00001519443,0.0000323092,0.0001201421,0.000009491833,0.00001335086,0.00007832047,0.00002270376,0.9824462,0.001317591,0.01497059,0.0009654682,0.000008646155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05427168,0.001620384,0.93937,0.0004675763,0.00007558522,0.00008782367,0.0001016366,0.0004234349,0.003581861],"genre_scores_gemma":[0.8155155,0.00144537,0.1777788,0.0002198127,0.0001129252,0.0001681131,0.0001773259,0.000130085,0.004452024],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003459022,"threshold_uncertainty_score":0.01597023,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0925739385889539,"score_gpt":0.1810849314283024,"score_spread":0.0885109928393485,"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."}}