{"id":"W3103197541","doi":"","title":"Reconfigurable fractional microwave signal processor based on a microcomb","year":2019,"lang":"en","type":"preprint","venue":"Figshare","topic":"Advanced Fiber Laser Technologies","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Australian Research Council; 1000 Talents Sichuan Program; Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"Differentiator; Hilbert transform; Microwave; Passband; Signal processing; Computer science; Electronic engineering; Bandwidth (computing); SIGNAL (programming language); Fractional Fourier transform; Fourier transform; Digital signal processing; Telecommunications; Mathematics; Band-pass filter; Computer hardware; Spectral density; Engineering; Mathematical analysis","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0000256742,0.00043499,0.0003815329,0.0001402262,0.00009070626,0.0001230045,0.0005932978,0.0003351559,0.2100755],"category_scores_gemma":[0.00004365179,0.0004420883,0.0002345699,0.0001076119,0.00001160748,0.00009916651,0.0002456221,0.001395593,0.006235558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009725564,"about_ca_system_score_gemma":0.0004163357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009130425,"about_ca_topic_score_gemma":9.562086e-7,"domain_scores_codex":[0.9983739,0.00002233035,0.0002680796,0.0007367135,0.0002231681,0.0003757958],"domain_scores_gemma":[0.9985394,0.0001572419,0.0003939439,0.0006546242,0.0001964503,0.00005831568],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001057363,0.0004346524,0.0004421282,0.00125847,0.0002079444,0.00001237526,0.00004790751,0.05812333,0.002132962,0.0001715347,0.9135269,0.02353607],"study_design_scores_gemma":[0.001531866,0.0001853924,0.0005160529,0.01247123,0.00006092088,0.000002185834,0.0001476639,0.01359012,0.3866706,0.02207177,0.5605087,0.002243473],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.0008888704,0.000171563,0.002319346,0.0005924065,0.0002766725,0.002091842,0.8855991,0.0006678738,0.1073924],"genre_scores_gemma":[0.6986371,3.249845e-7,0.001808237,0.0002635344,0.0003678363,0.001533512,0.2947322,0.00009233603,0.002565002],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.6977482,"threshold_uncertainty_score":0.9998031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.026306054643962,"score_gpt":0.2603568779299761,"score_spread":0.2340508232860141,"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."}}