{"id":"W2063190708","doi":"10.1109/mass.2014.29","title":"Towards Smart Routing: Exploiting User Context for Video Delivery in Mobile Networks","year":2014,"lang":"en","type":"article","venue":"","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Computer network; Exploit; Scheduling (production processes); Triangular routing; Routing (electronic design automation); Static routing; Schedule; Distributed computing; Quality of experience; Quality of service; Routing protocol; Computer security","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":[0.001513704,0.0008942249,0.001093857,0.0005472226,0.0008442071,0.001304131,0.001396245,0.001366437,0.000515398],"category_scores_gemma":[0.004030085,0.0005388916,0.0004918276,0.0007625706,0.0009814645,0.002949968,0.001714895,0.0012289,0.0001304051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001078327,"about_ca_system_score_gemma":0.0008222077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002654974,"about_ca_topic_score_gemma":0.003818169,"domain_scores_codex":[0.9989803,0.0005156927,0.00004209513,0.000147824,0.0001805376,0.0001335677],"domain_scores_gemma":[0.9985614,0.0008654649,0.0001613604,0.0001786408,0.0001367182,0.00009623404],"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":[0.0002836337,0.0001327321,0.001822721,0.0002930725,0.00009037295,0.0003051873,0.0004284453,0.7711807,0.02063759,0.0933208,0.003173306,0.1083315],"study_design_scores_gemma":[0.000009602845,0.00005839902,0.0001639007,0.000008453728,0.00001460344,0.00007450979,0.00006988642,0.976436,0.001272353,0.02061177,0.001268581,0.00001196844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0422066,0.001606651,0.953315,0.0006775844,0.00006182301,0.00008953907,0.00004106703,0.0001918852,0.001809925],"genre_scores_gemma":[0.7729447,0.001868338,0.2235277,0.0001845756,0.0001408584,0.00008413327,0.00006471387,0.00007822192,0.001106592],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002654974,"threshold_uncertainty_score":0.008005321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01987393898947718,"score_gpt":0.2307170505412641,"score_spread":0.2108431115517869,"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."}}