{"id":"W1968633182","doi":"10.1109/jlt.2013.2263278","title":"Echelle Diffractive Grating Based Wavelength Interrogator for Potential Aerospace Applications","year":2013,"lang":"en","type":"article","venue":"Journal of Lightwave Technology","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Department of National Defence; Institute for Microstructural Sciences; University of Ottawa","funders":"","keywords":"Fiber Bragg grating; Aerospace; Structural health monitoring; Interrogation; Optical fiber; Grating; Electronic engineering; Fiber optic sensor; Diffraction grating; Optics; Repeatability; Materials science; Engineering; Electrical engineering; Optoelectronics; Aerospace engineering; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008247554,0.0001666868,0.0002944835,0.0003609912,0.00006805922,0.00002058934,0.0002503985,0.0002289986,0.00005329909],"category_scores_gemma":[0.00009374051,0.0001495467,0.0001215079,0.0002580631,0.00007228194,0.0001901005,0.00002763993,0.0004411082,0.00002528383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000141379,"about_ca_system_score_gemma":0.00002684288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.277646e-7,"about_ca_topic_score_gemma":0.000001421272,"domain_scores_codex":[0.9990342,0.000009091337,0.0004351235,0.0001281321,0.0001169682,0.0002765269],"domain_scores_gemma":[0.9990087,0.0001380486,0.0002513251,0.0002250575,0.0003008677,0.00007603459],"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":[0.00007467757,0.000323724,0.0004048865,0.0001856771,0.0006068279,0.00004199048,0.0005063931,0.02255537,0.8701439,0.004838721,0.008984647,0.09133319],"study_design_scores_gemma":[0.003101829,0.0007935166,0.0003084938,0.0001647658,0.0001793369,0.0004634711,0.003115965,0.2135504,0.7319809,0.0203813,0.02518819,0.0007718381],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2504852,0.0002346442,0.7454935,0.002224705,0.0003364033,0.0006208298,0.00001060777,0.0002994157,0.0002946631],"genre_scores_gemma":[0.8419523,0.00002478256,0.1576231,0.00003618986,0.0001405912,0.00009881445,0.000001641703,0.00004345544,0.00007915153],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5914671,"threshold_uncertainty_score":0.6098336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00542210726632462,"score_gpt":0.2193282492814489,"score_spread":0.2139061420151243,"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."}}