{"id":"W1507863016","doi":"10.1109/lpt.2015.2455525","title":"Frequency Response Enhancement by Periodical Nonuniform Sampling in Distributed Sensing","year":2015,"lang":"en","type":"article","venue":"IEEE Photonics Technology Letters","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Reflectometry; Optical time-domain reflectometer; Frequency response; Distributed acoustic sensing; Optical fiber; Fiber optic sensor; Optics; Sampling (signal processing); Frequency domain; Range (aeronautics); Dynamic range; Fiber; Time domain; Electronic engineering; Materials science; Computer science; Physics; Polarization-maintaining optical fiber; Engineering; Electrical engineering; Detector","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":[0.0003587407,0.0002831427,0.0002016249,0.0002819636,0.0001360976,0.0002015309,0.0003754263,0.0002354788,0.0004161549],"category_scores_gemma":[0.0009097682,0.0001668814,0.0001102992,0.0003310466,0.0003699464,0.0005520931,0.000338501,0.0001866405,0.0001155547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002343444,"about_ca_system_score_gemma":0.0001048141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000276362,"about_ca_topic_score_gemma":0.0004272924,"domain_scores_codex":[0.999709,0.00005842672,0.00001319625,0.00009592051,0.00009510964,0.00002834454],"domain_scores_gemma":[0.9994172,0.0002651245,0.0001089628,0.000106529,0.00007611006,0.00002607739],"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":[0.0001583604,0.00005013007,0.000776982,0.00005870062,0.000005522847,0.00007078736,0.00008286345,0.004074783,0.9416645,0.000958032,0.00009452038,0.05200477],"study_design_scores_gemma":[0.00003200403,0.0004441966,0.003528736,0.000008916173,0.00002105206,0.0004242553,0.00004211081,0.1762421,0.815556,0.001228941,0.002437103,0.00003464126],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7271424,0.001081546,0.268647,0.0001105558,0.00005393325,0.00003575761,0.00003273339,0.0004343242,0.00246181],"genre_scores_gemma":[0.9567025,0.0001793442,0.04255834,0.00003280439,0.00002823361,0.00001655225,0.00001779235,0.00001713876,0.000447157],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0004161549,"threshold_uncertainty_score":0.001897275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01477141308234706,"score_gpt":0.2420496422298848,"score_spread":0.2272782291475378,"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."}}