{"id":"W4296831502","doi":"10.3390/s22197145","title":"Coaxial Mach–Zehnder Digital Strain Sensor Made from a Tapered Depressed Cladding Fiber","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Financiadora de Estudos e Projetos; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Ministério da Ciência, Tecnologia e Inovação; Fundo para o Desenvolvimento Tecnológico das Telecomunicações; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Materials science; Optical fiber; Optics; Extinction ratio; Cladding (metalworking); Free spectral range; Mach–Zehnder interferometer; Interferometry; Fiber optic sensor; Insertion loss; Coaxial; Optoelectronics; Electrical engineering; Physics; Wavelength; Engineering; Composite material","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":[0.0001999898,0.0003943139,0.0003094306,0.000319063,0.0001421007,0.0003248466,0.0008402137,0.0004127161,0.000451998],"category_scores_gemma":[0.0003805295,0.0002583252,0.0001193651,0.0002182928,0.0002961318,0.0004704399,0.0001833638,0.00030171,0.0001730567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004008691,"about_ca_system_score_gemma":0.0003118692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006021053,"about_ca_topic_score_gemma":0.001203284,"domain_scores_codex":[0.9996985,0.00002295115,0.00001400396,0.00009396054,0.0001537162,0.00001677248],"domain_scores_gemma":[0.9996089,0.00005651728,0.0001114164,0.00005482776,0.0001384598,0.00002991077],"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":[0.00008938715,0.00003922596,0.001195121,0.0001063381,0.000008020222,0.00007317396,0.00002193974,0.001254841,0.9816126,0.0004074732,0.00014173,0.01505008],"study_design_scores_gemma":[0.00001987229,0.0003540348,0.004352998,0.000009692687,0.00002754422,0.0005247173,0.00002247957,0.05106422,0.9412642,0.00009635431,0.002233015,0.00003107881],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7743027,0.0008344702,0.2200777,0.0001647365,0.0001570848,0.0001507483,0.0003943345,0.0009669178,0.002951239],"genre_scores_gemma":[0.8783097,0.0002936465,0.1196299,0.00004879228,0.00002765392,0.00004330906,0.0001342495,0.00001029907,0.001502414],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008402137,"threshold_uncertainty_score":0.002908528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01072793033585777,"score_gpt":0.2069145014813149,"score_spread":0.1961865711454571,"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."}}