{"id":"W4377971274","doi":"10.1109/jlt.2023.3279449","title":"Deep Learning-Aided Perturbation Model-Based Fiber Nonlinearity Compensation","year":2023,"lang":"en","type":"article","venue":"Journal of Lightwave Technology","topic":"Optical Network Technologies","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); University of British Columbia","funders":"Alliance de recherche numérique du Canada; University of British Columbia","keywords":"Nonlinear system; Computation; Computer science; Computational complexity theory; Nonlinear distortion; Artificial neural network; Signal processing; Quantization (signal processing); Perturbation (astronomy); Algorithm; Artificial intelligence; Theoretical computer science; Telecommunications; Physics","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":[0.0007343107,0.0007434177,0.0007047236,0.0003636178,0.0003167019,0.0005921481,0.001263219,0.0007476108,0.001527913],"category_scores_gemma":[0.001687548,0.0004123398,0.0004780124,0.0004099165,0.0005722904,0.001201324,0.001057333,0.001461904,0.0004904488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001073547,"about_ca_system_score_gemma":0.00152581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005851908,"about_ca_topic_score_gemma":0.007971948,"domain_scores_codex":[0.9996188,0.0000926223,0.00001691314,0.00007356891,0.0001442378,0.00005390527],"domain_scores_gemma":[0.9994465,0.0002383854,0.00005662559,0.00008431831,0.0001469958,0.00002725786],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006843458,0.00005722608,0.0002869158,0.00003771838,0.0000266524,0.00002989842,0.0000251679,0.906918,0.006651445,0.004049994,0.001264229,0.08058427],"study_design_scores_gemma":[0.000001387094,0.000004642442,0.00001266051,7.008932e-7,9.277235e-7,0.000002481424,6.872187e-7,0.9984444,0.001066213,0.0003992992,0.00006537898,0.000001224302],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02242097,0.0001987463,0.9738642,0.000197197,0.00003065393,0.00003207303,0.00006662541,0.00136699,0.001822498],"genre_scores_gemma":[0.6798142,0.0002452041,0.3142423,0.0002462979,0.00004236515,0.0001072028,0.0003864771,0.0001574924,0.004758602],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005851908,"threshold_uncertainty_score":0.01163566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01261719357542335,"score_gpt":0.2323648199151556,"score_spread":0.2197476263397323,"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."}}