{"id":"W2727608015","doi":"10.1109/tvt.2017.2720481","title":"Cross-Layer Optimization of Fast Video Delivery in Cache- and Buffer-Enabled Relaying Networks","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Huawei Technologies (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China; Alexander von Humboldt-Stiftung","keywords":"Computer science; Cache; Computer network; Online algorithm; Video quality; Optimization problem; Wireless network; Channel (broadcasting); Real-time computing; Wireless; Algorithm","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0002248314,0.0001579321,0.000247845,0.000433518,0.0004166854,0.0001358844,0.0006659706,0.0003198186,0.000005303012],"category_scores_gemma":[0.00001729101,0.0001584797,0.0000745546,0.0002585323,0.0002031691,0.0004759359,0.0000191528,0.0004636957,0.00000301756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005132884,"about_ca_system_score_gemma":0.00002900073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002836449,"about_ca_topic_score_gemma":0.00009444312,"domain_scores_codex":[0.9988747,0.00004328841,0.0002768273,0.0004138667,0.0001340817,0.0002572534],"domain_scores_gemma":[0.9987735,0.00004935418,0.0001511406,0.0008855936,0.00009976159,0.00004065594],"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":[0.00003496711,0.0001198235,0.001930901,0.00001484293,0.00004371768,0.00005033483,0.00008033513,0.9373514,0.005456713,0.0007651222,0.000004983861,0.05414686],"study_design_scores_gemma":[0.0008606591,0.0001124661,0.001119755,0.00009026613,0.00001868059,0.0000452503,0.00003480717,0.9866963,0.01063816,0.0001735784,0.00002348773,0.0001866164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3794703,0.000144515,0.6195581,0.0003601325,0.000198671,0.00009862004,0.000001436033,0.000114451,0.00005374449],"genre_scores_gemma":[0.9951794,0.0002152813,0.00440808,0.00005382632,0.000009551182,0.00002803476,6.059319e-7,0.00001223102,0.00009303021],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.615709,"threshold_uncertainty_score":0.6462614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01384704794695149,"score_gpt":0.2403735125181116,"score_spread":0.2265264645711602,"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."}}