{"id":"W1849761680","doi":"","title":"Hierarchical optimization procedure for traffic grooming in WDM optical networks","year":2009,"lang":"en","type":"article","venue":"Optical Network Design and Modelling","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Traffic grooming; Wavelength-division multiplexing; Heuristic; Routing and wavelength assignment; Computer science; Network topology; Routing (electronic design automation); Multiplexing; Computer network; Topology (electrical circuits); Mesh networking; Mathematical optimization; Wavelength; Mathematics; Telecommunications; Optics; Artificial intelligence; Physics","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":[0.001202533,0.0005248917,0.0005869533,0.0008090864,0.0005767187,0.0005902858,0.0008735142,0.000789246,0.004225143],"category_scores_gemma":[0.002429586,0.0003587253,0.0007429057,0.0007973266,0.0005447691,0.0007134822,0.001120802,0.001023148,0.000705449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008104047,"about_ca_system_score_gemma":0.001421859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004462966,"about_ca_topic_score_gemma":0.005631061,"domain_scores_codex":[0.9995307,0.0001844878,0.00002052843,0.00005457173,0.0001375807,0.00007215425],"domain_scores_gemma":[0.9994729,0.0003213655,0.00004412028,0.00004248978,0.00009310115,0.00002612716],"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":[0.00007384019,0.00007216036,0.0003857609,0.0001480783,0.00002786445,0.00007521703,0.0001252478,0.8480552,0.005602282,0.05215796,0.002108346,0.09116808],"study_design_scores_gemma":[0.000005259881,0.00001857473,0.00005885663,0.000005348253,0.000003659997,0.000006538602,0.000009841512,0.9927811,0.0004705202,0.006020921,0.0006156031,0.000003705909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007319139,0.0001110516,0.9899878,0.00005626493,0.00001663502,0.00006585004,0.00004512696,0.0001723121,0.002225884],"genre_scores_gemma":[0.2051882,0.0002555021,0.7897837,0.00009170478,0.00003903468,0.0003447447,0.0002640731,0.0001954032,0.003837576],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004462966,"threshold_uncertainty_score":0.01413447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01744072078354426,"score_gpt":0.2209983212656173,"score_spread":0.203557600482073,"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."}}