{"id":"W2802631035","doi":"10.1364/cleo_at.2018.jtu2a.55","title":"Nyquist-WDM Super-Channel Using an On-Chip Frequency Comb enabled by a Silicon Dual-drive MZM","year":2018,"lang":"en","type":"article","venue":"Conference on Lasers and Electro-Optics","topic":"Optical Network Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Citation; Wavelength-division multiplexing; Computer science; Chip; Frequency comb; Channel (broadcasting); Silicon; Silicon chip; Optoelectronics; Telecommunications; Electronic engineering; Laser; Physics; Engineering; World Wide Web; Optics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000110219,0.0003887999,0.0003720252,0.0001241018,0.0002005157,0.00015115,0.0002398178,0.0003179454,0.00004831724],"category_scores_gemma":[0.00006534694,0.000371165,0.00004678534,0.0002395136,0.0003539403,0.000177635,0.00004681581,0.0005127393,0.00003879853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000970526,"about_ca_system_score_gemma":0.00004580929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002995158,"about_ca_topic_score_gemma":0.00004170937,"domain_scores_codex":[0.9982677,0.00003312599,0.0002735272,0.0004358866,0.0002006823,0.0007890823],"domain_scores_gemma":[0.9990688,0.00009426376,0.00004298157,0.0004564063,0.0001303446,0.0002072033],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002852686,0.0007179693,0.0006337633,0.0002399512,0.0003795263,0.0001220068,0.001390508,0.004845872,0.6202906,0.3541021,0.005537735,0.01145466],"study_design_scores_gemma":[0.001111109,0.00427481,0.00009717581,0.0002096937,0.00008662654,0.00002463098,0.0006002775,0.832552,0.1382533,0.02122097,0.0004255825,0.001143877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9899015,0.0001403279,0.001469993,0.0002501882,0.000214858,0.000238714,0.00003207495,0.0006205077,0.007131868],"genre_scores_gemma":[0.9971301,0.0004793062,0.00183474,0.0002151874,0.0001629523,0.00001506365,0.00002994891,0.00005994512,0.00007274576],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.827706,"threshold_uncertainty_score":0.9998741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0231255627202203,"score_gpt":0.2377130166366534,"score_spread":0.2145874539164331,"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."}}