{"id":"W4402570575","doi":"10.1109/jphot.2024.3462438","title":"Coherent all Optical Reservoir Computing for Equalization of Impairments in Coherent Fiber Optic Communication Systems","year":2024,"lang":"en","type":"article","venue":"IEEE photonics journal","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Japan Society for the Promotion of Science","keywords":"Computer science; Optical fiber; Equalization (audio); Fiber-optic communication; Fiber optic splitter; Optical communication; Reservoir computing; Optics; Electronic engineering; Telecommunications; Fiber optic sensor; Physics; Decoding methods; Artificial neural network; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001609571,0.0001698175,0.0001974672,0.0001090755,0.0001993154,0.0003124207,0.0004640383,0.000265163,0.0008337902],"category_scores_gemma":[0.0003176726,0.00008067992,0.0001166323,0.0001541222,0.0003468177,0.0006397475,0.0004211963,0.0003926052,0.00009081192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003101669,"about_ca_system_score_gemma":0.0003840595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000615193,"about_ca_topic_score_gemma":0.001226248,"domain_scores_codex":[0.9999373,0.00001094152,0.000002832494,0.00001069386,0.00002345426,0.00001477038],"domain_scores_gemma":[0.9999013,0.00004000873,0.00001694473,0.00001256165,0.00001841808,0.00001068483],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000399346,0.000270126,0.001431842,0.0002428158,0.00007389405,0.0002728992,0.0001983224,0.1432189,0.6780539,0.06977496,0.001301617,0.1047613],"study_design_scores_gemma":[0.00001325719,0.0001373114,0.0002551227,0.000009973114,0.00001374767,0.00005525793,0.00001843628,0.8813797,0.1121353,0.004740205,0.00122439,0.00001739091],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5440294,0.001700515,0.4444673,0.0007072894,0.0001012823,0.00005048569,0.00004885994,0.0005471973,0.008347683],"genre_scores_gemma":[0.9681172,0.0001904703,0.0304272,0.00005721584,0.00001046587,0.00001419627,0.00001146419,0.00001573114,0.001156011],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008337902,"threshold_uncertainty_score":0.002789259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04026894684511397,"score_gpt":0.323605222965741,"score_spread":0.2833362761206271,"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."}}