{"id":"W4210427263","doi":"10.1109/globecom46510.2021.9685928","title":"Learning-based Cache Placement and Content Delivery for Satellite-Terrestrial Integrated Networks","year":2021,"lang":"en","type":"article","venue":"2021 IEEE Global Communications Conference (GLOBECOM)","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Cache; Backhaul (telecommunications); Markov decision process; Content delivery; Benchmark (surveying); Key (lock); Content delivery network; Computer network; Distributed computing; Markov process; Server; Base station; 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":[0.0009576273,0.0005631861,0.0009028866,0.0003349728,0.0003665742,0.0005585457,0.001379959,0.0009073733,0.001266089],"category_scores_gemma":[0.002776258,0.0003686958,0.0002918079,0.0005843769,0.0006299969,0.001039563,0.0007520696,0.001084186,0.0001389209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001962381,"about_ca_system_score_gemma":0.002269998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01984297,"about_ca_topic_score_gemma":0.01681513,"domain_scores_codex":[0.9997098,0.00007304879,0.0000133816,0.00007307505,0.00005274473,0.00007791205],"domain_scores_gemma":[0.998988,0.0006219207,0.0001220007,0.00003890572,0.000156253,0.00007304137],"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":[0.0000720372,0.00007101383,0.0008623156,0.00003336754,0.00001570345,0.00002718452,0.00001973514,0.9700363,0.0006409395,0.002246835,0.0007200908,0.02525455],"study_design_scores_gemma":[0.000003578343,0.000010138,0.00003826098,8.674551e-7,0.000002183144,0.000002155336,0.000002042313,0.9992656,0.0001106107,0.0005274818,0.00003645036,7.316521e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1727145,0.001119208,0.8217275,0.0007992119,0.00008042505,0.00008603296,0.0001319429,0.0006176297,0.00272346],"genre_scores_gemma":[0.9530131,0.000261756,0.04405572,0.0001626454,0.00003179178,0.00006602416,0.0001299009,0.00003261592,0.002246404],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01984297,"threshold_uncertainty_score":0.03945494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08690335063813394,"score_gpt":0.2848747265372762,"score_spread":0.1979713758991423,"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."}}