{"id":"W2015691841","doi":"10.1364/jon.7.000378","title":"Resilient traffic grooming for WDM networks","year":2008,"lang":"en","type":"article","venue":"Journal of Optical Networking","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Traffic grooming; Computer network; Integer programming; Routing (electronic design automation); Routing and wavelength assignment; Computer science; Network topology; Wavelength-division multiplexing; Distributed computing; Linear programming; Topology (electrical circuits); Engineering; Wavelength; Algorithm","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":[0.0003610049,0.0002414415,0.0004989919,0.0001291888,0.0001596345,0.00003061687,0.0003539723,0.0002310975,0.000006329979],"category_scores_gemma":[0.0001568407,0.0002144637,0.0002594031,0.0003470682,0.0001658152,0.0002089156,0.00005329025,0.0007208326,0.000001819479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001331116,"about_ca_system_score_gemma":0.00001953437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.31496e-8,"about_ca_topic_score_gemma":8.470628e-7,"domain_scores_codex":[0.9980594,0.00001159382,0.0008211916,0.0001621236,0.0002717591,0.0006739201],"domain_scores_gemma":[0.9986337,0.0007124237,0.0001432381,0.0002012837,0.0001232158,0.0001860863],"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":[0.00006270712,0.00003193894,0.00008659608,0.00001647477,0.00006316284,0.00007051356,0.00002597546,0.949497,0.0002522601,0.0008423084,0.001886521,0.0471645],"study_design_scores_gemma":[0.0008791813,0.0002866886,0.000275668,0.0002316462,0.00005920619,0.0006880582,0.00006417587,0.9774844,0.0001765614,0.0005560789,0.01895279,0.000345527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1972205,0.004205444,0.7955129,0.0001309831,0.001815017,0.000192975,6.296669e-7,0.0003039616,0.000617619],"genre_scores_gemma":[0.7967218,0.001096472,0.2000648,0.00004229385,0.001990998,0.000007553842,9.280527e-7,0.00005902696,0.00001611402],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5995013,"threshold_uncertainty_score":0.8745575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0195023664036284,"score_gpt":0.2334484091542965,"score_spread":0.2139460427506681,"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."}}