{"id":"W2400159820","doi":"10.1109/jlt.2016.2569073","title":"Convex Channel Power Optimization in Nonlinear WDM Systems Using Gaussian Noise Model","year":2016,"lang":"en","type":"article","venue":"Journal of Lightwave Technology","topic":"Optical Network Technologies","field":"Engineering","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ciena (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Convex optimization; Signal-to-noise ratio (imaging); Channel (broadcasting); Optimization problem; Noise power; Gaussian noise; Wavelength-division multiplexing; Amplifier; Margin (machine learning); Mathematical optimization; Electronic engineering; Topology (electrical circuits); Computer science; Mathematics; Power (physics); Telecommunications; Engineering; Regular polygon; Algorithm; Physics; Bandwidth (computing); Electrical engineering; Optics","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":[0.001036266,0.0009021793,0.0008708644,0.000462252,0.0003339696,0.00103268,0.0005608829,0.0006444996,0.001060926],"category_scores_gemma":[0.002556049,0.0005046601,0.0003834066,0.0006307392,0.001380298,0.001212731,0.0007705703,0.0006009492,0.0002065639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002087896,"about_ca_system_score_gemma":0.001323836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006356305,"about_ca_topic_score_gemma":0.005442431,"domain_scores_codex":[0.9994086,0.0002395589,0.00001251591,0.00006565041,0.0001700226,0.0001036842],"domain_scores_gemma":[0.998738,0.000932496,0.000102572,0.00004527678,0.0001451983,0.00003641569],"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":[0.00001632886,0.000007395671,0.00008699023,0.00001437394,0.00000653802,0.00002039173,0.00001355834,0.9908068,0.0006635079,0.006417192,0.0001405326,0.001806498],"study_design_scores_gemma":[0.000001833568,0.000005551679,0.00002691283,0.000001262351,0.000001085292,0.000003051143,0.000003376902,0.9972989,0.0002241257,0.002375418,0.0000564296,0.000002018647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07609968,0.0004802537,0.9148536,0.0003444165,0.00002148016,0.00004115961,0.00008908596,0.0001274285,0.007942823],"genre_scores_gemma":[0.9441823,0.0005149058,0.04971587,0.00008255204,0.00003207171,0.00008255622,0.00008880654,0.00008402552,0.00521697],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006356305,"threshold_uncertainty_score":0.01514876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01270562682013461,"score_gpt":0.2221794535438514,"score_spread":0.2094738267237168,"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."}}