{"id":"W3010964076","doi":"10.1364/ofc.2020.th4c.6","title":"Demonstration of photonic neural network for fiber nonlinearity compensation in long-haul transmission systems","year":2020,"lang":"en","type":"article","venue":"","topic":"Optical Network Technologies","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Compensation (psychology); Photonics; Transmission (telecommunications); Artificial neural network; Nonlinear system; Computer science; Photonic-crystal fiber; Optical fiber; Electronic engineering; Optics; Telecommunications; Physics; 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":[0.00008244313,0.0000908775,0.0001807603,0.00002373716,0.00001222451,0.00001321518,0.0000840273,0.0001212144,0.00001705424],"category_scores_gemma":[0.00001294875,0.00008440977,0.00003876697,0.0002190522,0.00002198477,0.00008134679,0.000009280501,0.0001258036,0.000003716966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002254339,"about_ca_system_score_gemma":0.000006618361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000128965,"about_ca_topic_score_gemma":0.00002409291,"domain_scores_codex":[0.9993483,0.00001258603,0.0002825741,0.0001120828,0.00007790251,0.0001665786],"domain_scores_gemma":[0.9997466,0.00008882588,0.00002295778,0.00008164945,0.00002496894,0.00003501773],"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":[0.00002279448,0.00001079439,0.001591326,0.0002129313,0.000007743785,8.612046e-7,0.00002165628,0.9880247,0.0009520949,0.001201059,0.000332477,0.007621567],"study_design_scores_gemma":[0.0002753339,0.00006749441,0.002078329,0.00004081222,0.000007648305,8.364158e-7,0.00002171783,0.995438,0.001623981,0.00005732491,0.0003015278,0.00008697331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8455175,0.00056923,0.1502865,0.0005529364,0.0001540006,0.0009107993,0.000007302895,0.0006862962,0.001315496],"genre_scores_gemma":[0.9670848,0.00002786755,0.0327443,0.00001615165,0.00005935111,0.0000198989,0.0000239123,0.0000139949,0.00000977536],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1215673,"threshold_uncertainty_score":0.3442131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0195919765721928,"score_gpt":0.2295115781855062,"score_spread":0.2099196016133134,"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."}}