{"id":"W2112762862","doi":"10.1109/glocom.2007.448","title":"Heuristics for Planning GMPLS Networks with Conversion and Regeneration Capabilities","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":"University of Ottawa","funders":"","keywords":"Heuristics; Computer science; Scalability; Integer programming; Multiprotocol Label Switching; Granularity; Routing (electronic design automation); Computer network; Distributed computing; Linear programming; Mathematical optimization; Quality of service; Algorithm; Mathematics","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":[0.001269982,0.001453115,0.001089272,0.0009750424,0.0006347658,0.00123583,0.001083756,0.001228512,0.002534911],"category_scores_gemma":[0.003239475,0.001209003,0.0006447269,0.001111069,0.001137736,0.001044862,0.0009443819,0.0009775236,0.0002430669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001782012,"about_ca_system_score_gemma":0.002303861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01103808,"about_ca_topic_score_gemma":0.01414063,"domain_scores_codex":[0.9993919,0.000301288,0.00002756267,0.00008107968,0.00008517011,0.0001130398],"domain_scores_gemma":[0.9978562,0.001759188,0.0001405632,0.00004419517,0.0001035921,0.00009619377],"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":[0.00004780963,0.00002670764,0.0001585021,0.00005096366,0.00001706429,0.00005070772,0.00003176357,0.9860644,0.0001664252,0.006697581,0.0006068457,0.006081116],"study_design_scores_gemma":[0.00004820043,0.0000297602,0.00006544621,0.00001591997,0.00001117184,0.00001116473,0.0000546884,0.9886467,0.000218592,0.01014004,0.000751588,0.000006741453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08214051,0.001578898,0.9002923,0.0009710974,0.0001233959,0.0005861023,0.0006746607,0.0006335564,0.01299952],"genre_scores_gemma":[0.521232,0.0008298238,0.4736706,0.0001968533,0.00005733015,0.0006573295,0.0006665002,0.000109406,0.002580102],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01103808,"threshold_uncertainty_score":0.02194762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008949278067475927,"score_gpt":0.2174474092497852,"score_spread":0.2084981311823093,"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."}}