{"id":"W4226476934","doi":"10.1364/ofc.2022.w2a.35","title":"Perturbation-aided deep neural network for dual-polarization optical communication systems","year":2022,"lang":"en","type":"article","venue":"Optical Fiber Communication Conference (OFC) 2022","topic":"Optical Network Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); University of British Columbia; Memorial University of Newfoundland","funders":"","keywords":"Artificial neural network; Computer science; Perturbation (astronomy); Multiplexing; Nonlinear system; Polarization (electrochemistry); Optical communication; Communications system; Neural system; Electronic engineering; Physics; Optics; Artificial intelligence; Telecommunications; Neuroscience; Engineering; Chemistry","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":[0.0009034629,0.0003876331,0.0004953607,0.0001535838,0.001149425,0.0003395526,0.001579684,0.0002745647,0.0007258849],"category_scores_gemma":[0.00044035,0.0004421757,0.0001511096,0.0008256365,0.0003218009,0.0004345883,0.001102251,0.001292248,0.00009282896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003773806,"about_ca_system_score_gemma":0.00006135441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001721159,"about_ca_topic_score_gemma":0.00002580293,"domain_scores_codex":[0.9970303,0.0003988376,0.0008781438,0.0004357351,0.0005572141,0.0006997897],"domain_scores_gemma":[0.9953648,0.001787207,0.0001518853,0.00217245,0.0003496437,0.0001739946],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004950733,0.0001090026,0.0001484169,0.0000603426,0.00009027336,0.000001327753,0.0001518863,0.3837837,0.0003212726,0.6019647,0.00341123,0.009908438],"study_design_scores_gemma":[0.0005686803,0.0001496632,0.0003750793,0.00004436609,0.00008440814,0.00002301194,0.0006516919,0.9579063,0.00005937334,0.008678556,0.03092204,0.0005368332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2846961,0.04945754,0.4224175,0.0445848,0.005613021,0.01753024,0.0007698113,0.01673794,0.1581931],"genre_scores_gemma":[0.9358835,0.0004176428,0.05909547,0.0001571824,0.0001043116,0.001809094,0.001377195,0.0001053848,0.001050202],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6511874,"threshold_uncertainty_score":0.999803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02301413190263681,"score_gpt":0.2357846408528581,"score_spread":0.2127705089502213,"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."}}