{"id":"W2802494570","doi":"10.1139/tcsme-2007-0001","title":"POTENTIAL OF BRAGG GRATING SENSORS FOR AIRCRAFT HEALTH MONITORING","year":2007,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Department of National Defence","funders":"","keywords":"Aerospace; Structural health monitoring; Fiber Bragg grating; Robustness (evolution); Computer science; Optical fiber; Systems engineering; Automotive engineering; Engineering; Aerospace engineering; Telecommunications; Electrical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0003476394,0.0001623252,0.0002629021,0.00006970653,0.0001797308,0.00000717849,0.0001889809,0.0001478489,0.000003548857],"category_scores_gemma":[0.00003948564,0.0001694616,0.0005970166,0.0002583553,0.00002688613,0.00007253154,0.000003141984,0.0002271968,1.342309e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004341925,"about_ca_system_score_gemma":0.00009554262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001289263,"about_ca_topic_score_gemma":0.002694899,"domain_scores_codex":[0.9987711,0.000003446543,0.0004286129,0.0001407855,0.0001444856,0.000511596],"domain_scores_gemma":[0.9992376,0.0001708273,0.00005938394,0.0002265279,0.00007523721,0.0002304261],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005008451,0.00000630085,0.000002160594,0.0003356882,0.0001337342,1.391094e-7,0.0003090138,0.9554321,0.04055103,0.0005665594,0.00002956618,0.002628762],"study_design_scores_gemma":[0.0007872388,0.00009588683,0.0001095425,0.0001990044,0.0001075562,0.00001221665,0.0008958532,0.7167836,0.2793754,0.0002136932,0.001076534,0.0003434411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04847255,0.0001241503,0.9493974,0.0001625726,0.001007306,0.0005349782,0.0001850517,0.0001089475,0.000007044644],"genre_scores_gemma":[0.8449246,0.00001347219,0.1548388,0.00001458571,0.00009112944,0.00002208698,0.000002961182,0.00006421457,0.00002823926],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.796452,"threshold_uncertainty_score":0.6910443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01023248766251358,"score_gpt":0.2297261152747798,"score_spread":0.2194936276122662,"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."}}