{"id":"W3190564786","doi":"10.1364/ofc.2021.th4j.5","title":"Forecasting Lightpath QoT with Deep Neural Networks","year":2021,"lang":"en","type":"article","venue":"","topic":"Optical Network Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ciena (Canada); École de Technologie Supérieure","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence; Long short term memory; Transmission (telecommunications); Deep learning; Field (mathematics); Deep neural networks; Multilayer perceptron; Perceptron; Machine learning; Recurrent neural network; Telecommunications","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":[],"consensus_categories":[],"category_scores_codex":[0.0000284763,0.0001209716,0.0001255576,0.0000223909,0.00003628225,0.00004634804,0.00009973451,0.00008655581,0.00008479384],"category_scores_gemma":[0.00002497916,0.00009374497,0.00002666962,0.0003028785,0.00002985266,0.00008075159,0.00006225499,0.0002213549,0.000008580163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002058829,"about_ca_system_score_gemma":0.000002900419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001006161,"about_ca_topic_score_gemma":0.00006039563,"domain_scores_codex":[0.9993426,0.000005201221,0.0001108452,0.0001402749,0.00007889781,0.0003221613],"domain_scores_gemma":[0.9996389,0.00006535672,0.000008107697,0.0002102402,0.00003176673,0.00004567471],"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":[0.000002992538,0.000008337202,0.00154081,0.00001600511,0.00002926671,0.0001872104,0.0000150265,0.9323132,0.00003988541,0.003914571,0.0004549878,0.06147772],"study_design_scores_gemma":[0.0001009491,0.00002800514,0.0001725367,0.00001281195,0.000008476476,0.00005902749,0.00006977717,0.9981168,0.0007218813,0.0001511479,0.0004125697,0.0001460019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5502846,0.001893259,0.3687016,0.0004078936,0.0004950592,0.0001503832,6.899255e-7,0.004699522,0.07336695],"genre_scores_gemma":[0.9493878,0.00002279492,0.05028664,0.00005669734,0.00009592614,0.000007803188,0.000003209344,0.00003053142,0.0001085678],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3991032,"threshold_uncertainty_score":0.3822809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01099344581060875,"score_gpt":0.1756571452814732,"score_spread":0.1646636994708645,"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."}}