{"id":"W4386593678","doi":"10.3390/photonics10080864","title":"Investigation of Hybrid Remote Fiber Optic Sensing Solutions for Railway Applications","year":2023,"lang":"en","type":"article","venue":"Photonics","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Optiwave Systems (Canada)","funders":"","keywords":"Fiber Bragg grating; Computer science; Optical fiber; Fiber optic sensor; SIGNAL (programming language); Wavelength; Photodetector; Electronic engineering; Optics; Telecommunications; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004060138,0.0003192392,0.0001904531,0.0004493037,0.0001911889,0.0004162425,0.0005493045,0.0005853862,0.001179058],"category_scores_gemma":[0.0003169635,0.000144613,0.0003866367,0.0003302509,0.0001944424,0.0007275829,0.0003452899,0.0001904667,0.0003235278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002831786,"about_ca_system_score_gemma":0.0001490763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004068678,"about_ca_topic_score_gemma":0.0006689302,"domain_scores_codex":[0.9996289,0.00003627908,0.0000133907,0.00007196561,0.0002087781,0.00004072662],"domain_scores_gemma":[0.9998548,0.00002666735,0.00003989356,0.00001342582,0.00005506057,0.00001006173],"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":[0.0001154007,0.00006807206,0.001051668,0.0003000674,0.00003003582,0.0002194055,0.0000914021,0.005194228,0.9604023,0.001939423,0.000324845,0.03026329],"study_design_scores_gemma":[0.00003108193,0.002354291,0.00466405,0.00006377573,0.00007518173,0.0006467968,0.0003094362,0.100054,0.8737836,0.0006130033,0.01735432,0.00005044979],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9076816,0.003480489,0.07661238,0.0002887947,0.0001244956,0.0001209279,0.00009934915,0.0003257713,0.01126618],"genre_scores_gemma":[0.9545698,0.001015455,0.04034486,0.00008898568,0.00002298438,0.00005016259,0.00007236796,0.00002334404,0.003811917],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001179058,"threshold_uncertainty_score":0.003944397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03254754106162545,"score_gpt":0.2497137626924136,"score_spread":0.2171662216307882,"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."}}