{"id":"W2112566494","doi":"10.1364/ofc.2011.owi1","title":"Energy Efficient Grooming of Scheduled Sub-wavelength Traffic Demands","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Traffic grooming; Energy consumption; Computer science; Computer network; Set (abstract data type); Energy (signal processing); Efficient energy use; Wavelength; Distributed computing; Wavelength-division multiplexing; Engineering; Electrical engineering; Optoelectronics; Materials science; 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":[0.0004149454,0.0005146244,0.000471079,0.0002354452,0.0002947828,0.0006045028,0.0007277577,0.0004336734,0.00158513],"category_scores_gemma":[0.001199768,0.0003122959,0.0002978786,0.0004150585,0.0002725397,0.0008853501,0.0004075086,0.0004898594,0.0001035071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004248137,"about_ca_system_score_gemma":0.0005833833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001857821,"about_ca_topic_score_gemma":0.004388597,"domain_scores_codex":[0.9998043,0.00005393302,0.000006665325,0.00003258605,0.00005221099,0.00005031508],"domain_scores_gemma":[0.9994819,0.0003321929,0.00006593633,0.00003346998,0.00005304634,0.00003359576],"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":[0.0001219293,0.0000933498,0.000498415,0.0001046327,0.00002677505,0.0001122442,0.00007004658,0.9337609,0.01613869,0.02242709,0.0008834744,0.02576245],"study_design_scores_gemma":[0.000004627354,0.00003102018,0.0001925449,0.000003064242,0.000005917973,0.00001419496,0.00001811195,0.9924197,0.001663299,0.005342996,0.0003012605,0.000003254934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1669676,0.0003232796,0.8244514,0.0004715894,0.00006906743,0.000106089,0.0002113695,0.000123259,0.00727633],"genre_scores_gemma":[0.9205457,0.0002985988,0.07543457,0.00006129834,0.00004184208,0.00006620015,0.0001026633,0.00005288618,0.003396306],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001857821,"threshold_uncertainty_score":0.005302787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01364241909080552,"score_gpt":0.1871251123804804,"score_spread":0.1734826932896748,"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."}}