{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004122488,0.0003624037,0.0004234426,0.0003503831,0.0003050287,0.0003039569,0.003175942,0.000369618,0.000005530168],"category_scores_gemma":[0.00003448931,0.0004405731,0.0002764997,0.000220775,0.00009701558,0.0006726543,0.003512714,0.001357232,0.00004367382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003359715,"about_ca_system_score_gemma":0.0002130801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003178959,"about_ca_topic_score_gemma":0.0006803238,"domain_scores_codex":[0.9977196,0.0002144292,0.0002136179,0.001272458,0.00009494201,0.0004849799],"domain_scores_gemma":[0.9974233,0.0001315758,0.0003256704,0.001851215,0.0001111283,0.0001571196],"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":[0.00004371547,0.00006390705,0.004929406,0.0000154947,0.00006492384,0.001425618,0.0001555582,0.9494467,0.000006195387,0.04064576,0.0002068828,0.002995867],"study_design_scores_gemma":[0.0004420493,0.00003353014,0.002507959,0.0002474373,0.00003011706,0.000005992472,0.00004770331,0.9866443,0.00000332611,0.009477865,0.00007341267,0.000486346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3140338,0.0003657023,0.6792912,0.00007491971,0.001211413,0.0002229471,0.000006916635,0.0002306265,0.004562476],"genre_scores_gemma":[0.9978895,0.0003634421,0.0002656297,0.0001138233,0.0001245737,0.000001029063,0.000008166342,0.00001778619,0.001216073],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6838557,"threshold_uncertainty_score":0.9998046,"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."}}