{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005047564,0.0003135533,0.0004003219,0.00006356241,0.0005943907,0.0007201293,0.001740557,0.0001663056,0.00002890112],"category_scores_gemma":[0.0002397763,0.0003235667,0.0001926961,0.0005165292,0.0001930638,0.0003022092,0.0007046582,0.0004749459,0.00002004559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002347111,"about_ca_system_score_gemma":0.0006935306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000593532,"about_ca_topic_score_gemma":0.00123806,"domain_scores_codex":[0.9974532,0.0006496315,0.0005402113,0.0006289033,0.0002635346,0.0004645608],"domain_scores_gemma":[0.9963961,0.0005809708,0.0002203948,0.001903743,0.0006860014,0.0002128353],"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":[0.0007699519,0.002094992,0.0236617,0.0001080748,0.0009817252,0.0001015827,0.0009659008,0.04232231,0.004541944,0.1136389,0.00964692,0.801166],"study_design_scores_gemma":[0.002217439,0.0002623896,0.001110294,0.0001739299,0.0000860721,0.00002757172,0.00099447,0.9687728,0.0002838971,0.0003106791,0.02523468,0.0005257707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08353762,0.006236542,0.9002991,0.005448592,0.001265518,0.0007745265,0.0001101708,0.0002966884,0.00203125],"genre_scores_gemma":[0.9844887,0.001948687,0.01204536,0.0006399192,0.000066624,0.0001263788,0.0003375609,0.00001185559,0.0003349249],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9264505,"threshold_uncertainty_score":0.9999216,"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."}}