{"id":"W2904283960","doi":"10.1109/spects.2018.8574197","title":"Dash-Based Device-to-Device Video Streaming for Cellular Networks with High User Density","year":2018,"lang":"en","type":"article","venue":"","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Universidade Federal de Pernambuco; Ministry of Education, India; Ministry of Earth Sciences","keywords":"Computer science; Dynamic Adaptive Streaming over HTTP; Computer network; Quality of experience; Cellular network; DEVS; Video quality; Implementation; Architecture; Quality of service; Dash; Multimedia; Operating system; Simulation","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":[0.0008774164,0.0003549075,0.0003918249,0.000263455,0.0004954025,0.001085925,0.0009550806,0.0005611342,0.001023294],"category_scores_gemma":[0.001166612,0.0002012554,0.0003999345,0.0002858396,0.0004895899,0.0009146912,0.0005636259,0.0006404668,0.0002576026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001114341,"about_ca_system_score_gemma":0.0008168287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005363334,"about_ca_topic_score_gemma":0.006557962,"domain_scores_codex":[0.9994977,0.0002123946,0.0000410459,0.00006510171,0.0001302913,0.00005349332],"domain_scores_gemma":[0.9992377,0.000255806,0.00005949752,0.0001382225,0.0002629744,0.00004583393],"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":[0.000734381,0.000465354,0.008029837,0.000592499,0.0001448857,0.001397127,0.0007642081,0.5921898,0.08643455,0.1824274,0.005192182,0.1216277],"study_design_scores_gemma":[0.00001651402,0.0001086901,0.0004002315,0.00001171324,0.00002574147,0.00008387648,0.00004592021,0.9842489,0.007219815,0.004593556,0.003234021,0.00001106346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1618435,0.0005830336,0.8260018,0.0003887194,0.00008456614,0.000278404,0.0002011293,0.002462235,0.008156683],"genre_scores_gemma":[0.9292932,0.000391862,0.0661438,0.0001012559,0.00002383894,0.00010584,0.0002681871,0.00005935627,0.00361262],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005363334,"threshold_uncertainty_score":0.01066422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0156289361955167,"score_gpt":0.2213345097681752,"score_spread":0.2057055735726585,"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."}}