{"id":"W2791890302","doi":"10.1109/tvt.2018.2817210","title":"Cache-Enabled Adaptive Video Streaming Over Vehicular Networks: A Dynamic Approach","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Carleton University","funders":"","keywords":"Computer science; Cache; Computer network; Backhaul (telecommunications); Real-time computing; Video quality; Base station; Channel (broadcasting); Wireless; Telecommunications","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":[0.0004590057,0.0005525118,0.0006260954,0.0004465696,0.0003604902,0.0006396475,0.001138343,0.0004652171,0.0003725265],"category_scores_gemma":[0.00113401,0.0002231873,0.0002893773,0.0005211892,0.0004116905,0.0007733352,0.0005707981,0.000520098,0.00006556304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008470434,"about_ca_system_score_gemma":0.0008894323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005815039,"about_ca_topic_score_gemma":0.006359284,"domain_scores_codex":[0.9997225,0.00006596,0.00001318214,0.00005498491,0.00008316144,0.00006008757],"domain_scores_gemma":[0.9995049,0.0001967703,0.00007276386,0.00004266119,0.0001334051,0.00004941598],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008462108,0.00004150221,0.0007867478,0.0000526574,0.00003573792,0.0001000127,0.00005181211,0.9432507,0.008350898,0.01070957,0.0004231145,0.0361126],"study_design_scores_gemma":[0.000002800757,0.00002116641,0.00007550035,0.000001783814,0.000005198134,0.00001937078,0.00001038024,0.997983,0.0004919099,0.001191785,0.0001934369,0.00000367512],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08169177,0.00109615,0.9138086,0.0002277311,0.00005084059,0.00003998115,0.00003670399,0.0002273416,0.002820804],"genre_scores_gemma":[0.9733568,0.0003448736,0.02543674,0.00003298786,0.0000211498,0.00002176588,0.00002777507,0.00001836762,0.0007395432],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005815039,"threshold_uncertainty_score":0.01156235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009455320865318804,"score_gpt":0.2166325175251136,"score_spread":0.2071771966597948,"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."}}