{"id":"W2159200357","doi":"10.1016/j.osn.2007.09.002","title":"Algorithms for the global design of WDM networks including the traffic grooming","year":2007,"lang":"en","type":"article","venue":"Optical Switching and Networking","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Traffic grooming; Computer science; Heuristic; Wavelength-division multiplexing; Tabu search; Node (physics); Network topology; Computer network; Routing (electronic design automation); Topology (electrical circuits); Distributed computing; Metaheuristic; Set (abstract data type); Algorithm; Mathematics; Wavelength","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":[0.002599902,0.001636577,0.001316547,0.0008601061,0.001035856,0.001648212,0.001758268,0.00148557,0.00540987],"category_scores_gemma":[0.00536734,0.0007355308,0.001035466,0.00123866,0.001100368,0.002875995,0.002261403,0.002285265,0.001188684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001064237,"about_ca_system_score_gemma":0.001375808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009266325,"about_ca_topic_score_gemma":0.002129547,"domain_scores_codex":[0.9991925,0.0003111877,0.00004622539,0.0001239709,0.0002346176,0.00009140816],"domain_scores_gemma":[0.9981747,0.001024829,0.0001311742,0.0003068419,0.0003036181,0.00005887068],"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":[0.0001195312,0.00007109084,0.0003142612,0.0001990308,0.00007110413,0.00003532979,0.000154701,0.5238203,0.002865303,0.2156598,0.006510685,0.2501788],"study_design_scores_gemma":[0.00003798881,0.00005573854,0.00006155911,0.00002810557,0.00003372479,0.00002923069,0.00001688105,0.8509803,0.000956581,0.1426737,0.00511203,0.00001410341],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00130562,0.0001611473,0.9967428,0.00006502606,0.00003025438,0.00003268936,0.00001698723,0.0001422153,0.001503387],"genre_scores_gemma":[0.09949786,0.0008581336,0.8946113,0.0002009791,0.000164293,0.0004388985,0.0002162168,0.0002433508,0.003769113],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00540987,"threshold_uncertainty_score":0.01809782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03480953123892663,"score_gpt":0.2738892268364651,"score_spread":0.2390796955975385,"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."}}