{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003664475,0.0003598673,0.0002244468,0.0001049861,0.0001487861,0.0003291307,0.0003933044,0.0004108901,0.0008637572],"category_scores_gemma":[0.001136172,0.0001535899,0.0001404249,0.00009702487,0.0003167348,0.0006243613,0.000437613,0.0005450636,0.0002021647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000325877,"about_ca_system_score_gemma":0.0003522559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001528691,"about_ca_topic_score_gemma":0.003983878,"domain_scores_codex":[0.999889,0.00002337687,0.000005864976,0.00001842684,0.00003564639,0.00002771496],"domain_scores_gemma":[0.9997318,0.0001305536,0.00003472329,0.00002708766,0.00006462316,0.00001127347],"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":[0.0002034595,0.0001343852,0.001181184,0.00008080682,0.00005389254,0.0001249232,0.00007120908,0.7961408,0.09622099,0.01018873,0.0008896078,0.09471],"study_design_scores_gemma":[0.000003133768,0.00003687028,0.00007887738,0.000002100824,0.000003744299,0.000008558037,0.00000249829,0.9898009,0.009038645,0.000908316,0.0001135603,0.000002899317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3603109,0.0004221257,0.6311627,0.0003895181,0.00005675446,0.00002349169,0.00005772925,0.001317891,0.006258884],"genre_scores_gemma":[0.9684682,0.00006330632,0.02992432,0.00005271881,0.000008032836,0.00001179157,0.0000313122,0.00001763397,0.001422615],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001528691,"threshold_uncertainty_score":0.003039539,"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."}}