{"id":"W7096525568","doi":"","title":"Recommended Citation Jabbar, Syed, "An Efficient Scheme for Non-bifurcated Traffic Grooming in WDM Networks " (2010). Electronic Theses and Dissertations. Paper 340. AN EFFICIENT SCHEME FOR NON-BIFURCATED TRAFFIC GROOMING IN WDM NETWORKS","year":2010,"lang":"en","type":"article","venue":"","topic":"Williams Syndrome Research","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Windsor; Scheme (mathematics); Traffic grooming; Permission; Wavelength-division multiplexing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003966794,0.00112885,0.001183929,0.001510723,0.001081854,0.0007232714,0.001298773,0.0008936127,0.0001826687],"category_scores_gemma":[0.002167682,0.001094845,0.000303591,0.003203128,0.0003824694,0.001005847,0.0001983416,0.002197515,0.00002511247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000587846,"about_ca_system_score_gemma":0.0004580466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003149894,"about_ca_topic_score_gemma":0.01636891,"domain_scores_codex":[0.9904418,0.0005696803,0.0018716,0.002712674,0.000943297,0.003460944],"domain_scores_gemma":[0.9933721,0.003497331,0.0005380178,0.001383644,0.0004477237,0.0007611609],"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":[0.00189271,0.00345591,0.00147674,0.0003175548,0.00006184328,0.00001551787,0.005003351,0.6224599,0.3418592,0.001056543,0.0003278152,0.02207295],"study_design_scores_gemma":[0.005282099,0.0007031579,0.006347267,0.0002424534,0.00004853515,0.00005202483,0.001444807,0.9802388,0.003821789,0.00007307007,0.0004570204,0.001289006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9701134,0.0001301007,0.02175347,0.0007966121,0.0007733729,0.005948319,0.00002880472,0.0003877435,0.00006818288],"genre_scores_gemma":[0.9838353,0.00009438096,0.01243276,0.0002702716,0.0002192833,0.001890241,0.0005384712,0.0003077356,0.0004115167],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3577789,"threshold_uncertainty_score":0.9991502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04151056856212262,"score_gpt":0.3200412669841101,"score_spread":0.2785306984219874,"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."}}