{"id":"W1504387104","doi":"10.1007/978-3-540-74742-0_71","title":"Strategies for Traffic Grooming over Logical Topologies","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Traffic grooming; Computer science; Network topology; Computer network; Throughput; Bandwidth (computing); Integer programming; Distributed computing; Logical topology; Wavelength-division multiplexing; Topology (electrical circuits); Algorithm; Telecommunications; 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":[0.0004412689,0.0007474743,0.0004099661,0.0007664915,0.0006241216,0.001353443,0.001490177,0.0005743959,0.005999238],"category_scores_gemma":[0.001417318,0.0003176469,0.0003495209,0.0007192731,0.000530328,0.001573764,0.001167532,0.0006435648,0.0009469949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004831589,"about_ca_system_score_gemma":0.0004130949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004279604,"about_ca_topic_score_gemma":0.001206112,"domain_scores_codex":[0.9998087,0.00004796562,0.00001145163,0.00002751475,0.00006430665,0.00004003582],"domain_scores_gemma":[0.9995207,0.0002365279,0.00004419452,0.00007272089,0.00008408222,0.00004181999],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001623696,0.0001936458,0.0004450855,0.0005158259,0.00006021021,0.000182168,0.000217139,0.1520256,0.02777459,0.4435045,0.009639774,0.365279],"study_design_scores_gemma":[0.00003276348,0.0001286791,0.000156681,0.00005668965,0.00005369362,0.0002611081,0.0001257191,0.6779256,0.006238419,0.3021638,0.0128314,0.00002550944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01623414,0.0009122901,0.9589032,0.0003168237,0.0001326686,0.0001278994,0.00005842636,0.0004156579,0.02289886],"genre_scores_gemma":[0.4969479,0.002277854,0.4800506,0.0003280561,0.0001733583,0.0004249955,0.0001858003,0.0001978386,0.01941365],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005999238,"threshold_uncertainty_score":0.02006942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02756813372718491,"score_gpt":0.2683708419892715,"score_spread":0.2408027082620866,"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."}}