{"id":"W2099779686","doi":"10.1109/icc.2011.5962606","title":"A Branch, Price and Cut Approach for Optimal Traffic Grooming in WDM Optical Networks","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Solver; Wavelength-division multiplexing; Computer science; Traffic grooming; Integer programming; Mathematical optimization; Network topology; Integer (computer science); Binary number; Distributed computing; Computer network; 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":[0.002089555,0.00123968,0.001252526,0.001252941,0.00101737,0.001741916,0.001363723,0.001726651,0.006185918],"category_scores_gemma":[0.003701932,0.0009635203,0.001011568,0.002061656,0.001522011,0.002226257,0.00104066,0.00258922,0.0005414317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002071373,"about_ca_system_score_gemma":0.001810672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004129746,"about_ca_topic_score_gemma":0.005038147,"domain_scores_codex":[0.9992225,0.0003301447,0.00002742958,0.00009062579,0.0002411958,0.00008814844],"domain_scores_gemma":[0.9988776,0.0008544927,0.00006287968,0.00003138369,0.000125884,0.00004782475],"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":[0.00006166435,0.00007418163,0.0001658353,0.0001619003,0.00003191319,0.00008823731,0.00007601296,0.7868375,0.0009652153,0.1459247,0.003133372,0.06247936],"study_design_scores_gemma":[0.00001907671,0.00003184727,0.00005122124,0.00002443702,0.00001140432,0.00002287804,0.00001565293,0.8916481,0.0003942747,0.105238,0.002534243,0.000008906344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003856525,0.0006459479,0.9889492,0.0003506476,0.00005635829,0.00008113024,0.00005379925,0.00005773518,0.00594878],"genre_scores_gemma":[0.1694417,0.002379512,0.8192154,0.0002550421,0.0002079151,0.0004851239,0.0002074518,0.0001923929,0.00761553],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006185918,"threshold_uncertainty_score":0.02069396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01770082802321512,"score_gpt":0.2082201375886486,"score_spread":0.1905193095654334,"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."}}