{"id":"W4389545074","doi":"10.1109/icee59167.2023.10334680","title":"Conserving Power Consumption in Elastic Optical Networks Using Deep Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Power (physics); Computer science; Network topology; Power consumption; Consumption (sociology); Amplifier; Power demand; Optical switch; Term (time); Topology (electrical circuits); Telecommunications; Computer network; Electronic engineering; Electrical engineering; Engineering; Physics; Bandwidth (computing)","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":[0.0005169319,0.0005578694,0.0005075613,0.0003476463,0.0003337783,0.0006099392,0.001035683,0.0005860665,0.001267423],"category_scores_gemma":[0.001153929,0.000300918,0.0002605409,0.000436771,0.0005439651,0.001093569,0.0006869029,0.0006854286,0.0001353586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001036885,"about_ca_system_score_gemma":0.0008705591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00671854,"about_ca_topic_score_gemma":0.01084623,"domain_scores_codex":[0.9998734,0.00002100414,0.000006005856,0.00002757001,0.00003236389,0.00003968309],"domain_scores_gemma":[0.9996268,0.0002101927,0.00005047728,0.00002560224,0.00006453422,0.00002231243],"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":[0.00003788643,0.0000390735,0.0004119695,0.00002025738,0.00001233919,0.0000234708,0.000012814,0.9706858,0.0009566802,0.001995115,0.0004284067,0.02537607],"study_design_scores_gemma":[0.000001200901,0.000004212398,0.00001828078,9.366157e-7,0.000001063582,0.00000141336,0.000001412661,0.9991632,0.0001234402,0.0006498432,0.00003450559,6.697349e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.222026,0.001048232,0.7658392,0.0007107076,0.00008969136,0.00004478228,0.0001099517,0.0008871777,0.009244295],"genre_scores_gemma":[0.9690017,0.000160877,0.02830929,0.0001539954,0.00002360898,0.00003240135,0.00005730259,0.00003521433,0.002225773],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00671854,"threshold_uncertainty_score":0.01335889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01710710056456239,"score_gpt":0.24837850799688,"score_spread":0.2312714074323176,"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."}}