{"id":"W3158531327","doi":"10.1038/s41598-021-88572-2","title":"Engineering nanoparticle features to tune Rayleigh scattering in nanoparticles-doped optical fibers","year":2021,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Canada First Research Excellence Fund; Canada Excellence Research Chairs, Government of Canada; Canada Research Chairs; Fonds de recherche du Québec – Nature et technologies; Université Laval","keywords":"Rayleigh scattering; Materials science; Optical fiber; Nanoparticle; Scattering; Fabrication; Fiber; Doping; Nanotechnology; Light scattering; Optoelectronics; Distributed acoustic sensing; Optics; Fiber optic sensor; Composite material; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001299618,0.0003805582,0.0001445915,0.0001821147,0.0001547259,0.0002772117,0.0001827888,0.000268057,0.000336763],"category_scores_gemma":[0.0002156474,0.0001882542,0.000186025,0.00009365931,0.0002253536,0.000257761,0.0001920093,0.0002668402,0.0001886983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004035829,"about_ca_system_score_gemma":0.0002163282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007205461,"about_ca_topic_score_gemma":0.001868735,"domain_scores_codex":[0.9999123,0.000006653901,0.000007541886,0.00002791742,0.0000283203,0.00001731534],"domain_scores_gemma":[0.9998399,0.00003364634,0.00005842267,0.00001383857,0.00003616248,0.00001801562],"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":[0.00001411546,0.00001212805,0.0000702032,0.00001977064,0.000002205653,0.0000140844,0.00001014253,0.0002218505,0.9986485,0.0001189795,0.00001517507,0.0008528318],"study_design_scores_gemma":[0.000006482852,0.00006392862,0.000461385,0.000003335657,0.000005477591,0.00003751734,0.000007485836,0.002450892,0.996073,0.00002795873,0.0008576098,0.000004889268],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9872844,0.0003721604,0.009919432,0.00005635701,0.00003154364,0.00004479225,0.00006111694,0.0001181283,0.002112184],"genre_scores_gemma":[0.9871838,0.0002996254,0.01107401,0.00002824986,0.000009648762,0.00004419419,0.00005654043,0.00003202273,0.001271959],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007205461,"threshold_uncertainty_score":0.002928197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008488045941488227,"score_gpt":0.2199233158404201,"score_spread":0.2114352698989319,"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."}}