{"id":"W4388642348","doi":"10.1109/jlt.2023.3332484","title":"Parallel Neural Network Structures for Signal-to-Noise Ratio Estimation in Optical Fiber Communication Systems","year":2023,"lang":"en","type":"article","venue":"Journal of Lightwave Technology","topic":"Optical Network Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Optical fiber; Signal-to-noise ratio (imaging); Computer science; Artificial neural network; Optical communication; Signal processing; Electronic engineering; SIGNAL (programming language); Communications system; Noise (video); Telecommunications; Engineering; Artificial intelligence","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":[0.0003954236,0.0001714178,0.0004103874,0.0007433739,0.0000576809,0.00004639328,0.0005059164,0.0003972291,0.000007951589],"category_scores_gemma":[0.000256152,0.0001516847,0.00007024384,0.001182675,0.00008801898,0.0001505372,0.0001066233,0.0005452085,0.00002735742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001205675,"about_ca_system_score_gemma":0.00001742128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.454278e-7,"about_ca_topic_score_gemma":0.000004666029,"domain_scores_codex":[0.9985716,0.00002836062,0.0007076898,0.0001248993,0.0001629267,0.000404549],"domain_scores_gemma":[0.9989977,0.0003463007,0.0001397298,0.0003343339,0.0001253811,0.00005652607],"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":[0.00002998655,0.000009591505,0.000199977,0.00002936979,0.00003555137,0.00001651881,0.0000279482,0.9429557,0.0005876608,0.04285679,0.006074674,0.007176221],"study_design_scores_gemma":[0.0005475153,0.0002839729,0.001675773,0.0001361703,0.00002832877,0.00009055168,0.0001280141,0.9561368,0.0008039496,0.03788563,0.002079265,0.0002040884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8818871,0.001931808,0.1046297,0.007513002,0.0007980981,0.001112063,0.000006552799,0.001775598,0.0003461252],"genre_scores_gemma":[0.8835257,0.0001013475,0.1161374,0.00001794568,0.00009166625,0.00005821664,0.000005856722,0.00003301198,0.00002890448],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01318103,"threshold_uncertainty_score":0.6185524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01512328911580674,"score_gpt":0.2563184982182238,"score_spread":0.241195209102417,"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."}}