{"id":"W3092601442","doi":"10.1364/sppcom.2020.spth3i.4","title":"Pre-compensation for Attenuation-induced Distortions in NFT-based Optical Fiber Communication with EDFA","year":2020,"lang":"en","type":"article","venue":"OSA Advanced Photonics Congress (AP) 2020 (IPR, NP, NOMA, Networks, PVLED, PSC, SPPCom, SOF)","topic":"Optical Network Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Attenuation; Compensation (psychology); Optical communication; Fiber-optic communication; Optical fiber; Fourier transform; Optical amplifier; Nonlinear distortion; Computer science; Optics; Optical attenuator; Materials science; Electronic engineering; Dispersion-shifted fiber; Telecommunications; Physics; Fiber optic sensor; Bandwidth (computing); Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003484706,0.0008342353,0.001004652,0.0001784476,0.0003466126,0.0002078059,0.001084125,0.0006162086,0.0001458385],"category_scores_gemma":[0.0003130623,0.0008858301,0.0002332533,0.001277289,0.0003202099,0.0006667432,0.0002364668,0.001373948,0.0000506049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005691162,"about_ca_system_score_gemma":0.0001340023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001822284,"about_ca_topic_score_gemma":0.000340802,"domain_scores_codex":[0.995974,0.0001365329,0.001197448,0.001019682,0.0005111467,0.001161211],"domain_scores_gemma":[0.9963732,0.001126787,0.0003457117,0.001453024,0.0003175425,0.0003837671],"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":[0.0006150432,0.0001842319,0.0008369668,0.0001628487,0.0001648846,0.00002069331,0.0001691067,0.9761078,0.0008372462,0.00334993,0.001539593,0.01601166],"study_design_scores_gemma":[0.003840627,0.0003660238,0.001450663,0.0002819298,0.0001469647,0.000007527683,0.0001277394,0.9825131,0.002149055,0.0006174762,0.007405568,0.001093326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7926582,0.004434029,0.1805708,0.005194448,0.00179542,0.007856085,0.0002716789,0.00432157,0.002897796],"genre_scores_gemma":[0.8715703,0.000482407,0.124799,0.0003736299,0.0001472215,0.001507386,0.0008076683,0.0002431448,0.00006929585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07891212,"threshold_uncertainty_score":0.9993593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01282732295671458,"score_gpt":0.2353033417599058,"score_spread":0.2224760188031912,"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."}}