{"id":"W29703506","doi":"10.1007/978-1-4614-6555-3_32","title":"Application of Multivariate Statistically Based Algorithms for Civil Structures Anomaly Detection","year":2013,"lang":"en","type":"book-chapter","venue":"Conference proceedings of the Society for Experimental Mechanics","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Bridge (graph theory); Structural health monitoring; Fiber Bragg grating; Principal component analysis; Anomaly detection; Computer science; Multivariate statistics; Algorithm; Data mining; Fiber optic sensor; Structural engineering; Artificial intelligence; Optical fiber; Engineering; Machine learning","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008415923,0.0003617156,0.0004312531,0.00003326467,0.00008528023,0.00002466022,0.0003558648,0.0003354843,0.00002809706],"category_scores_gemma":[0.00003752896,0.0003276522,0.0005856198,0.00003609766,0.00008879801,0.0001030068,0.00006453335,0.0001932199,8.776066e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001817285,"about_ca_system_score_gemma":0.00003302286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004845623,"about_ca_topic_score_gemma":9.240674e-7,"domain_scores_codex":[0.9987445,8.975881e-7,0.0004594149,0.0003258891,0.0002409746,0.0002283654],"domain_scores_gemma":[0.9987228,0.00008681139,0.0004216543,0.0001638692,0.0005489459,0.00005592544],"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":[0.00003862829,0.00001867629,1.464492e-7,0.0007379215,0.00023602,8.474321e-9,0.0005529861,0.0003447358,0.7114233,0.2825442,0.0001421543,0.003961119],"study_design_scores_gemma":[0.0004595388,0.00014755,7.009298e-7,0.00009109238,0.0001067288,8.260567e-7,0.0003175407,0.4601987,0.4825548,0.05517474,0.000706319,0.0002414722],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001078975,0.0002150473,0.993021,0.00001537289,0.0004058624,0.003067141,0.0004630797,0.0001337368,0.001599778],"genre_scores_gemma":[0.7317566,0.00002193538,0.2656576,0.00002443518,0.00008996088,0.0006071182,0.00004411547,0.0001768521,0.001621376],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7306776,"threshold_uncertainty_score":0.9999176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01865449741796358,"score_gpt":0.2481150483353809,"score_spread":0.2294605509174174,"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."}}