{"id":"W1997741957","doi":"10.1007/s11107-009-0227-5","title":"Strategies for optimal logical topology design and traffic grooming","year":2009,"lang":"en","type":"article","venue":"Photonic Network Communications","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Traffic grooming; Set (abstract data type); Logical topology; Topology (electrical circuits); Integer programming; Network topology; Computer network; Distributed computing; Throughput; Routing (electronic design automation); Suite; Integer (computer science); Mathematical optimization; Algorithm; Wavelength-division multiplexing; Mathematics; Wireless","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":[0.0001921264,0.0001696496,0.0002309314,0.00003884068,0.0002743387,0.00005007396,0.0006049871,0.0001524088,0.000009560194],"category_scores_gemma":[0.00003036031,0.0001706095,0.00004736858,0.0002029361,0.0002727816,0.000139742,0.0001056566,0.0003294445,0.000002864481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004643666,"about_ca_system_score_gemma":0.00001864663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.563835e-7,"about_ca_topic_score_gemma":0.000009385478,"domain_scores_codex":[0.9990619,0.00004447715,0.0002426511,0.0001714278,0.00004747425,0.0004320904],"domain_scores_gemma":[0.9982893,0.0007890255,0.00003140853,0.0008046431,0.00002863552,0.00005701704],"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":[0.00001260471,0.00002208538,0.00000201042,0.000005155582,0.00001903809,6.494461e-7,0.00005651727,0.8422524,0.0001671732,0.1257624,0.0008410799,0.03085892],"study_design_scores_gemma":[0.0002372511,0.0001546207,0.00005021136,0.00001952481,0.00002387982,0.00001444129,0.0002505523,0.9211804,0.00003509106,0.06746492,0.01035985,0.0002092311],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01591436,0.01216518,0.966148,0.001276734,0.00009744327,0.0007648209,0.000003266255,0.001503705,0.002126534],"genre_scores_gemma":[0.5420689,0.00194546,0.4557433,0.00007130881,0.00003029204,0.0001126986,0.000008066775,0.00001330292,0.000006732554],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5261545,"threshold_uncertainty_score":0.6957254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03816941803725791,"score_gpt":0.2830027816898483,"score_spread":0.2448333636525904,"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."}}