{"id":"W4226059872","doi":"10.1364/ofc.2022.th1d.3","title":"Group-velocity Dispersion Compensation of Telecom Data Signals using Compact Discrete Phase Filters in Silicon","year":2022,"lang":"en","type":"article","venue":"Optical Fiber Communication Conference (OFC) 2022","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":"Dispersion (optics); Group velocity; Compensation (psychology); Optics; Group delay and phase delay; Silicon; Phase (matter); Group delay dispersion; Materials science; Phase compensation; Optical filter; Fiber Bragg grating; SIGNAL (programming language); Waveguide; Phase velocity; Dispersion-shifted fiber; Optical fiber; Telecommunications; Physics; Optoelectronics; Computer science; Phase noise; Fiber optic sensor; Bandwidth (computing)","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006851216,0.000234954,0.0004290982,0.0001867857,0.0002222929,0.00006391654,0.001834405,0.0001069385,0.00128685],"category_scores_gemma":[0.0001530128,0.0002612307,0.00006242126,0.0006455178,0.0002901086,0.0004241573,0.001505958,0.0008883587,0.00001339619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002732948,"about_ca_system_score_gemma":0.00004524461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001297847,"about_ca_topic_score_gemma":0.00007360506,"domain_scores_codex":[0.9979452,0.000301346,0.0006369087,0.0003506628,0.0004064038,0.0003595007],"domain_scores_gemma":[0.9971954,0.000641331,0.000125952,0.001887358,0.00006375329,0.00008624125],"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":[0.0006868824,0.002537244,0.004731572,0.0005143247,0.0004311877,0.00002787044,0.001847671,0.5083583,0.2159802,0.1016533,0.003463012,0.1597684],"study_design_scores_gemma":[0.0008825341,0.0001321212,0.001290238,0.00008486318,0.00003224906,0.000005399567,0.0007642623,0.992949,0.001252718,0.0009933928,0.001284293,0.0003289034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9878882,0.0005764703,0.007206806,0.001002856,0.00009384333,0.0005506479,0.0002664637,0.0003025296,0.002112253],"genre_scores_gemma":[0.986093,0.0001997231,0.0125845,0.00003165314,0.00001022055,0.00002572302,0.00100731,0.00003236941,0.00001544184],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4845907,"threshold_uncertainty_score":0.999984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07509554016333861,"score_gpt":0.3117888230929088,"score_spread":0.2366932829295702,"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."}}