{"id":"W4406738703","doi":"10.1364/optica.540409","title":"Accelerating Brillouin fiber sensing via destructive-interference-enabled precise raw data acquisition and nonredundant image denoising","year":2025,"lang":"en","type":"article","venue":"Optica","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Sichuan Province; National Natural Science Foundation of China","keywords":"Noise reduction; Interference (communication); Fiber; Materials science; Data acquisition; Acoustics; Computer vision; Artificial intelligence; Computer science; Physics; Composite material; Telecommunications","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":[0.0004744814,0.0006664504,0.0004135995,0.0005388447,0.0001849593,0.0004772773,0.0007563946,0.0005113586,0.0006505282],"category_scores_gemma":[0.0009762197,0.0003621325,0.0003092985,0.000558291,0.0007618861,0.001003736,0.0009542702,0.0007713065,0.0003290664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000390163,"about_ca_system_score_gemma":0.0003992411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005420834,"about_ca_topic_score_gemma":0.001066649,"domain_scores_codex":[0.9996116,0.00003819998,0.00001631122,0.000114369,0.0001824109,0.00003710811],"domain_scores_gemma":[0.9995446,0.0001489618,0.0001104896,0.00008685965,0.00008825913,0.00002080409],"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":[0.000105239,0.00004998872,0.0004990686,0.0001393372,0.00001261414,0.0001094095,0.0001510822,0.005495174,0.9481157,0.006026572,0.0004237157,0.03887208],"study_design_scores_gemma":[0.00001964665,0.00008847696,0.0006463422,0.00001461272,0.00001190644,0.0001820211,0.00002902441,0.1503002,0.8424774,0.002244296,0.003943377,0.00004266483],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2377802,0.0009052914,0.7548389,0.0003989877,0.0001075826,0.00007660687,0.0001673634,0.001248698,0.00447636],"genre_scores_gemma":[0.5701529,0.0009621512,0.4260773,0.0002173133,0.00006939146,0.0001341107,0.0002474997,0.0001915282,0.001947824],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0007563946,"threshold_uncertainty_score":0.002830803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01781053628323818,"score_gpt":0.2620931100999178,"score_spread":0.2442825738166796,"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."}}