{"id":"W2981216466","doi":"10.1109/mvt.2019.2936087","title":"Transmission Protocol Customization for Network Slicing: A Case Study of Video Streaming","year":2019,"lang":"en","type":"article","venue":"IEEE Vehicular Technology Magazine","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer network; Packet loss; Network traffic control; Scalable Video Coding; Network packet; Quality of experience; Cache; Linear network coding; Quality of service; Scalability; Operating system","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002475305,0.0005481492,0.0003886888,0.0005015432,0.001379359,0.001058006,0.001255504,0.001478555,0.00109229],"category_scores_gemma":[0.005419592,0.0002041942,0.0004213377,0.0008907281,0.0009688658,0.001254745,0.0005924795,0.0008801545,0.0001102555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001276068,"about_ca_system_score_gemma":0.0008956754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008437102,"about_ca_topic_score_gemma":0.008769923,"domain_scores_codex":[0.9985986,0.0005885253,0.00007454596,0.0001433231,0.0003823881,0.0002126754],"domain_scores_gemma":[0.9950983,0.003013372,0.0004362192,0.0005635126,0.0006045492,0.0002840706],"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":[0.001545732,0.001657271,0.05958876,0.001076167,0.0003258001,0.04345996,0.007694448,0.4793131,0.09665085,0.08209994,0.009675854,0.216912],"study_design_scores_gemma":[0.0001797312,0.002050257,0.01431842,0.0001415303,0.0002295485,0.01299367,0.005250917,0.8628093,0.05862974,0.0142313,0.02900766,0.0001578593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8787283,0.001044846,0.1078404,0.000640357,0.00008044068,0.0003737812,0.0001801709,0.0003610675,0.01075063],"genre_scores_gemma":[0.9673215,0.0003745014,0.03034111,0.00005768372,0.00003137424,0.00005546616,0.0001150245,0.00005000472,0.001653201],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008437102,"threshold_uncertainty_score":0.01677597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01226948365872469,"score_gpt":0.2601859982090314,"score_spread":0.2479165145503067,"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."}}