{"id":"W2008415200","doi":"10.1109/iscc.2007.4381624","title":"Feedback Control System for Scheduling of Wide-Area All-Photonic Networks","year":2007,"lang":"en","type":"article","venue":"Proceedings - IEEE Symposium on Computers and Communications/IEEE Symposium on Computers and Communications","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Scheduling (production processes); Reservation; Queue; Time division multiple access; Propagation delay; Gain scheduling; Computer network; Feedback control; Bandwidth (computing); Queueing theory; Round-robin scheduling; Real-time computing; Distributed computing; Dynamic priority scheduling; Control (management); Quality of service; Engineering; Control engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008880859,0.000715826,0.0009319012,0.0004741373,0.0009279524,0.0002779508,0.00300676,0.0004170669,5.793358e-7],"category_scores_gemma":[0.00002752978,0.0007492995,0.000235913,0.0006411647,0.0008680646,0.0003509205,0.0006596039,0.001014627,0.00000359497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002743563,"about_ca_system_score_gemma":0.00003279686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001360216,"about_ca_topic_score_gemma":0.00001258157,"domain_scores_codex":[0.9968085,0.00006535536,0.001273961,0.0006807106,0.0003233896,0.0008480904],"domain_scores_gemma":[0.9933504,0.002824357,0.0004664485,0.002568266,0.0004680316,0.0003224806],"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":[0.0007774644,0.001467771,0.002091789,0.001450545,0.00193056,0.000004815238,0.002509929,0.7332183,0.02646098,0.1868435,0.004515035,0.03872935],"study_design_scores_gemma":[0.001897894,0.0006249796,0.0001430999,0.001151486,0.0001816187,0.00003713946,0.0004817565,0.989223,0.001496269,0.0004004886,0.003575527,0.0007867563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05409769,0.004273237,0.9245751,0.006038534,0.001364673,0.003005054,0.00007414423,0.002057248,0.004514311],"genre_scores_gemma":[0.8681772,0.00769635,0.1230179,0.0005632422,0.00008744047,0.0002897724,0.00004423701,0.0001132342,0.00001063246],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8140795,"threshold_uncertainty_score":0.9994958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0182495857981422,"score_gpt":0.2425712686119905,"score_spread":0.2243216828138483,"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."}}