{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001929661,0.0002197012,0.0002272869,0.000127249,0.0001152865,0.0000380802,0.0002029467,0.0001434715,0.00001032529],"category_scores_gemma":[0.00007110222,0.0001804605,0.00005351292,0.0003572431,0.00008119854,0.0001971345,0.0000393402,0.0002165027,0.000005354892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007727436,"about_ca_system_score_gemma":0.00000306258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001348937,"about_ca_topic_score_gemma":0.00006542966,"domain_scores_codex":[0.9987409,0.00000316178,0.0002571055,0.0002462341,0.0001271947,0.0006253667],"domain_scores_gemma":[0.9992061,0.0004155318,0.00002909625,0.0002514878,0.00003410656,0.00006372674],"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":[0.0001985391,0.00003849386,0.0003867054,0.0001934699,0.0001413189,0.00004821322,0.0006486165,0.6558799,0.004539126,0.008158439,0.0006411861,0.329126],"study_design_scores_gemma":[0.003325811,0.0006523343,0.001297546,0.000459759,0.0001298852,0.0001064013,0.008816074,0.882332,0.02742394,0.003501209,0.06979529,0.002159723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4053195,0.0003329084,0.5872645,0.0000666822,0.00008485891,0.0002466461,9.080155e-7,0.002618158,0.004065838],"genre_scores_gemma":[0.5733013,0.00002069483,0.4264059,0.00003666588,0.00008574814,0.000018163,0.000002144489,0.00005547541,0.00007391502],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3269663,"threshold_uncertainty_score":0.7358965,"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."}}