{"id":"W2342910849","doi":"10.1109/lcomm.2016.2558144","title":"Adaptive Delivery in Caching Networks","year":2016,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Redundancy (engineering); Computer network; Content delivery; Upper and lower bounds; Transmission (telecommunications); Monte Carlo method; Distributed computing; Mathematical optimization; Telecommunications; Mathematics; Statistics","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":[0.0009987555,0.0005015865,0.00074012,0.000556846,0.0007172474,0.001027369,0.001177539,0.001185019,0.001258092],"category_scores_gemma":[0.005335181,0.0003546436,0.0003370044,0.001000844,0.0008786169,0.001473642,0.0007995714,0.0007603166,0.000211564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001785387,"about_ca_system_score_gemma":0.0006150373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003876039,"about_ca_topic_score_gemma":0.00209707,"domain_scores_codex":[0.9992932,0.0003243185,0.00002769321,0.0001040032,0.0001619611,0.00008875622],"domain_scores_gemma":[0.9976331,0.001675615,0.0001800716,0.0001623525,0.000292606,0.00005634718],"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":[0.0002063382,0.00004265134,0.0009285419,0.0002287572,0.0000542783,0.0003528371,0.0001809651,0.8344948,0.008149049,0.09758307,0.003268085,0.05451053],"study_design_scores_gemma":[0.0000124056,0.0000322528,0.0001118468,0.000009294841,0.00001171682,0.00008289341,0.00002311006,0.9835083,0.001004326,0.01400623,0.001188142,0.000009495211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07355768,0.003379507,0.9158048,0.0005880345,0.0001099989,0.00009079069,0.00009326533,0.0003881351,0.005987852],"genre_scores_gemma":[0.8927773,0.001918773,0.1001801,0.000168167,0.00009980262,0.0001307167,0.00009167282,0.00006425774,0.004569267],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003876039,"threshold_uncertainty_score":0.01295394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03457221836502291,"score_gpt":0.2380852665125429,"score_spread":0.20351304814752,"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."}}