{"id":"W2092415065","doi":"10.1364/josaa.28.001759","title":"Differential group delay prediction in optical fiber links","year":2011,"lang":"en","type":"article","venue":"Journal of the Optical Society of America A","topic":"Optical Network Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Kalman filter; Autoregressive model; Differential group delay; Noise (video); Series (stratigraphy); Fiber; Computer science; Filter (signal processing); Extended Kalman filter; Imitation; Group delay and phase delay; Time series; Autoregressive–moving-average model; Control theory (sociology); Taylor series; Optical fiber; Applied mathematics; Mathematics; Telecommunications; Statistics; Artificial intelligence; Polarization mode dispersion; Mathematical analysis; Materials science; Machine learning","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":[0.000649153,0.0003880304,0.000371048,0.0002984076,0.00033432,0.0004017429,0.000545009,0.0007982783,0.0005826551],"category_scores_gemma":[0.002088557,0.0002491342,0.0003344695,0.0003757451,0.000639593,0.0006991553,0.0004263962,0.0006528849,0.00005846412],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001014596,"about_ca_system_score_gemma":0.00057657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03532627,"about_ca_topic_score_gemma":0.01297369,"domain_scores_codex":[0.9998216,0.00004700454,0.000006649137,0.00003513333,0.00004219994,0.00004746228],"domain_scores_gemma":[0.9991455,0.0005231688,0.0001307481,0.00004550915,0.0001080313,0.0000470173],"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":[0.00002906227,0.00001488376,0.0009992224,0.000002866483,0.000003080642,0.00001885079,0.00001229328,0.9974626,0.000308103,0.0004126052,0.00002098654,0.000715342],"study_design_scores_gemma":[0.00000379192,0.00001276301,0.0002289388,6.474954e-7,0.000001447101,0.000001878408,0.000003542047,0.9992969,0.0003088371,0.0001261338,0.00001268207,0.000002338693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9854664,0.00003870656,0.01339377,0.00009741142,0.00001157792,0.000006450274,0.00004413073,0.00006616826,0.0008752801],"genre_scores_gemma":[0.998828,0.00001502084,0.0008827424,0.000005357249,0.000001274478,0.000003336422,0.00002165118,0.000002632883,0.0002399132],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03532627,"threshold_uncertainty_score":0.07024127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01075483137861978,"score_gpt":0.1966181202877623,"score_spread":0.1858632889091425,"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."}}