{"id":"W2529801842","doi":"10.1007/s13349-016-0196-1","title":"A fixed-order time series model for damage detection and localization","year":2016,"lang":"en","type":"article","venue":"Journal of Civil Structural Health Monitoring","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Series (stratigraphy); Structural health monitoring; Context (archaeology); Computer science; Time series; Automation; Algorithm; Grid; Mathematical optimization; Data mining; Structural engineering; Applied mathematics; Mathematics; Engineering; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000325176,0.0002169697,0.0003613031,0.0001924006,0.0002285513,0.00003980818,0.0001350192,0.0001214537,0.000005610371],"category_scores_gemma":[0.00009939546,0.0001559772,0.00006066544,0.0001539595,0.00004193082,0.0007422224,0.00002669741,0.0001939293,7.608824e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004852381,"about_ca_system_score_gemma":0.00007687067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001091789,"about_ca_topic_score_gemma":0.00001797793,"domain_scores_codex":[0.9984689,0.00003725174,0.00068485,0.0001443333,0.0002546488,0.0004099677],"domain_scores_gemma":[0.9989291,0.0001150272,0.0002995819,0.000138693,0.0002842735,0.0002333555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005641889,0.000008440318,0.01324883,0.002394705,0.0001220817,0.000007433748,0.001940273,0.04034416,0.03588995,0.0001578679,0.001069664,0.9042524],"study_design_scores_gemma":[0.003781367,0.002102828,0.1307906,0.003277253,0.00008846068,0.0007212113,0.0002245553,0.7432486,0.08564077,0.02571397,0.003071958,0.001338419],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8880491,0.0009180266,0.1079026,0.0002952579,0.00217717,0.0003610047,0.00001588975,0.0002646078,0.00001632619],"genre_scores_gemma":[0.9794084,0.000648395,0.01865664,0.00001569329,0.001148396,0.0000129997,5.932732e-7,0.00005255092,0.00005628362],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.902914,"threshold_uncertainty_score":0.6360564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01698246609481663,"score_gpt":0.2925860431432065,"score_spread":0.2756035770483898,"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."}}