{"id":"W4377082200","doi":"10.1364/ofc.2023.m1j.7","title":"Combined parametric and denoising passive amplification by FWM-based oversampling and Talbot-based decimation","year":2023,"lang":"en","type":"article","venue":"","topic":"Optical Network Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Oversampling; Decimation; Parametric statistics; Noise reduction; Bandwidth (computing); Waveform; Computer science; Dispersion (optics); Physics; Optics; Electronic engineering; Materials science; Artificial intelligence; Telecommunications; Mathematics; Radar; Engineering; Statistics","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":[0.0001011624,0.0001261744,0.0001317796,0.0002496148,0.00007533043,0.00009675348,0.00006141337,0.0001173205,0.000006867036],"category_scores_gemma":[0.0002328213,0.0001261736,0.00001657122,0.000838968,0.00006232262,0.0001041797,0.00002332762,0.0001071214,0.00001612202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005170187,"about_ca_system_score_gemma":0.000006120792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008666317,"about_ca_topic_score_gemma":0.00000259997,"domain_scores_codex":[0.999302,0.00001005159,0.0001674497,0.000187532,0.0001181388,0.0002148272],"domain_scores_gemma":[0.9991818,0.0005540036,0.00002743045,0.0001587209,0.00002766378,0.00005036282],"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":[0.00007985789,0.0001093626,0.03012315,0.0006688061,0.0001293101,0.00001334992,0.0001040861,0.6437086,0.06314503,0.02052144,0.01465643,0.2267406],"study_design_scores_gemma":[0.0004929152,0.00004197659,0.01014506,0.00002975275,0.00001491631,3.501564e-7,0.00007551353,0.9770862,0.01065752,0.001097473,0.0001774277,0.0001808956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8094304,0.0001275184,0.1881064,0.0003223525,0.00004985334,0.000180819,0.000005835574,0.001669798,0.0001070567],"genre_scores_gemma":[0.9843423,0.00005355902,0.0154358,0.00004407497,0.000007796449,0.000024683,0.00005861762,0.00002428124,0.000008916641],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3333776,"threshold_uncertainty_score":0.5145211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009234084564103101,"score_gpt":0.2076964692965316,"score_spread":0.1984623847324285,"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."}}