{"id":"W3144924060","doi":"10.1364/oe.423103","title":"Recurrent neural networks achieving MLSE performance for optical channel equalization","year":2021,"lang":"en","type":"article","venue":"Optics Express","topic":"Optical Network Technologies","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Intersymbol interference; Channel (broadcasting); Bit error rate; Equalization (audio); Phase-shift keying; Transmission (telecommunications); Artificial neural network; Transmitter; Modulation (music); Feed forward; Estimator; Electronic engineering; Telecommunications; Artificial intelligence; Physics; Mathematics; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001063105,0.0001939817,0.0002141346,0.00004303258,0.00009311667,0.0000951091,0.000212649,0.000194973,0.000008186515],"category_scores_gemma":[0.0001440508,0.0002058579,0.000068414,0.0002184257,0.00005051255,0.0001635553,0.0001413592,0.0002708548,0.000004925447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004738483,"about_ca_system_score_gemma":0.000007534867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.087308e-7,"about_ca_topic_score_gemma":7.640357e-7,"domain_scores_codex":[0.9988602,0.00001104807,0.0002695696,0.0002412909,0.0001414294,0.0004764022],"domain_scores_gemma":[0.9992801,0.0001672046,0.00002707905,0.000340364,0.0001048027,0.00008044075],"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":[0.00001189384,0.00002897182,0.00003682312,0.0001608825,0.00002567389,0.00000653673,0.0000500822,0.950651,0.001080293,0.009506763,0.0005546571,0.03788641],"study_design_scores_gemma":[0.0002260762,0.00005468318,0.00005071702,0.00008028807,0.00002287781,0.000006853939,0.0000492387,0.9937071,0.004447401,0.0001449081,0.0009609254,0.000248884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3491721,0.001215917,0.6448768,0.0001804051,0.001548681,0.0003285582,0.000009997503,0.0009905255,0.001676976],"genre_scores_gemma":[0.9448426,0.0006742479,0.05381548,0.00003372212,0.0003812662,0.00006221898,0.00006148106,0.00006370533,0.0000652274],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5956705,"threshold_uncertainty_score":0.8394641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02184019211421786,"score_gpt":0.2423849682163599,"score_spread":0.220544776102142,"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."}}