{"id":"W1988738482","doi":"10.1109/glocom.2007.445","title":"Maximizing Throughput for Traffic Grooming with Limited Grooming Resources","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Traffic grooming; Synchronous optical networking; Computer network; Computer science; Multiplexing; Throughput; Wavelength-division multiplexing; Offset (computer science); Channel (broadcasting); Traffic shaping; Statistical time division multiplexing; Distributed computing; Network traffic control; Network packet; Wireless; Telecommunications; Wavelength","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":[0.0006478402,0.001034568,0.0008970586,0.00055043,0.0007676537,0.001126487,0.001071253,0.0009474348,0.002325157],"category_scores_gemma":[0.003400904,0.0004113382,0.0004978281,0.0008764822,0.0008567203,0.001556686,0.001154643,0.0007933756,0.0003747879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001275315,"about_ca_system_score_gemma":0.0007818852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00120683,"about_ca_topic_score_gemma":0.001502467,"domain_scores_codex":[0.9993083,0.0002143077,0.00001899654,0.0001393033,0.0001466668,0.000172416],"domain_scores_gemma":[0.99888,0.000754785,0.0001326707,0.0001056843,0.00006818378,0.0000586995],"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":[0.0004063946,0.0001701198,0.0006279068,0.0002751104,0.00005264765,0.0001963796,0.0002060236,0.8873839,0.0258466,0.0294352,0.002734595,0.05266509],"study_design_scores_gemma":[0.00001800097,0.00004598834,0.0001540583,0.00001092966,0.000009209404,0.000062942,0.00003682732,0.9785656,0.00316243,0.01726613,0.0006605285,0.000007346173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1615968,0.0006554631,0.8262084,0.000613336,0.00004297933,0.0001430498,0.0001977081,0.0007531595,0.009789119],"genre_scores_gemma":[0.8148413,0.0004932155,0.1813234,0.0001545178,0.00006931353,0.0001793062,0.0001867851,0.0001471392,0.002605028],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002325157,"threshold_uncertainty_score":0.009253085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01237128129749608,"score_gpt":0.2188405067469215,"score_spread":0.2064692254494254,"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."}}