{"id":"W2967031885","doi":"10.1109/stict.2019.8789375","title":"Greening The Network Using Traffic Prediction and Link Rate Adaptation","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Energy consumption; Efficient energy use; Simulated annealing; Link (geometry); Traffic generation model; Heuristic; Real-time computing; Mathematical optimization; Computer network; Algorithm; Engineering; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004160824,0.000632242,0.0006250657,0.0003872211,0.000342176,0.0005153789,0.0007435154,0.0004825656,0.0004228211],"category_scores_gemma":[0.001021873,0.0003354846,0.0004137056,0.0004468121,0.0003343286,0.0008363796,0.0003989124,0.000613433,0.0001064207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006123515,"about_ca_system_score_gemma":0.0007139317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005364703,"about_ca_topic_score_gemma":0.006967728,"domain_scores_codex":[0.999774,0.00005573342,0.000008049816,0.00005896579,0.00006857061,0.00003468126],"domain_scores_gemma":[0.9996438,0.0001753997,0.00005533919,0.00005637993,0.00005240338,0.00001663707],"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":[0.000028698,0.00004021091,0.000489571,0.00001076594,0.00001631823,0.00001825381,0.00001815558,0.9677556,0.005308073,0.001140445,0.0001910906,0.02498283],"study_design_scores_gemma":[0.000001103901,0.000005346798,0.00006842968,7.064734e-7,0.000002243438,0.000003387032,0.000001749718,0.9989713,0.0005842856,0.0002949917,0.00006454821,0.000001915969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08100548,0.0001914154,0.9151207,0.0001566643,0.00003245425,0.00003655854,0.00003321675,0.0008043585,0.002619291],"genre_scores_gemma":[0.9108261,0.0001145119,0.0879791,0.00004992204,0.00001624208,0.00004260268,0.0000467337,0.00004679792,0.0008779206],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005364703,"threshold_uncertainty_score":0.01066697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01414629934225281,"score_gpt":0.1995412016696277,"score_spread":0.1853949023273748,"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."}}