{"id":"W2104783163","doi":"10.1109/iscc.2005.69","title":"Fair and Efficient Frame-Based Scheduling Algorithm for Multimedia Networks","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Latency (audio); Quality of service; Network packet; Scheduling (production processes); Computer network; The Internet; Voice over IP; Frame (networking); Real-time computing; Distributed computing; Multimedia; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006525154,0.0001298648,0.0001194185,0.00004689348,0.00005628376,0.00002363402,0.00004913615,0.00009459796,0.00001942884],"category_scores_gemma":[0.00001193477,0.0001314283,0.00002858049,0.0001093814,0.00002134623,0.00006354617,0.00001092814,0.00009934116,0.000004310478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004583895,"about_ca_system_score_gemma":0.000005216167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.880765e-7,"about_ca_topic_score_gemma":0.000003153692,"domain_scores_codex":[0.9993681,0.000004858563,0.0001530155,0.0001548833,0.00006711661,0.0002520048],"domain_scores_gemma":[0.9996358,0.0001284693,0.00001868561,0.0001069411,0.00003470736,0.00007546436],"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":[0.000001816442,0.000007591429,0.00001067735,0.00000610896,0.000005204155,1.444637e-7,0.00001420688,0.7264405,0.00002666892,0.00002912754,0.00006177216,0.2733962],"study_design_scores_gemma":[0.0006026005,0.00001182731,0.00002446856,0.00001870847,0.000007448317,5.38895e-7,0.00001244923,0.9973769,0.0007499732,0.000007724985,0.001024081,0.0001633357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002887163,0.000611243,0.995348,0.00004636683,0.0001842723,0.0002890619,0.000003251722,0.0004859367,0.0001447249],"genre_scores_gemma":[0.3025589,0.00003233034,0.6969599,0.00007082217,0.0002611754,0.00003833684,0.00002170781,0.00003451313,0.00002234515],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2996718,"threshold_uncertainty_score":0.5359491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004778106267950455,"score_gpt":0.2074041259876558,"score_spread":0.2026260197197053,"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."}}