{"id":"W4362474531","doi":"10.1016/j.ymssp.2023.110324","title":"Efficient structural model updating with spatially sparse modal data: A Bayesian perspective","year":2023,"lang":"en","type":"article","venue":"Mechanical Systems and Signal Processing","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hydro-Québec","funders":"","keywords":"Identifiability; Gibbs sampling; Bayesian probability; Bayesian inference; Sensitivity (control systems); Computer science; Modal; Algorithm; Hierarchy; Degrees of freedom (physics and chemistry); Finite element method; Mathematical optimization; Mathematics; Artificial intelligence; Machine learning; Engineering","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":[0.000350407,0.0002480073,0.0003164064,0.0001015537,0.0002786435,0.0001826657,0.0002607192,0.0001193557,0.000002528397],"category_scores_gemma":[0.00002061456,0.000191775,0.00001955005,0.000328792,0.00003493535,0.0001687774,0.0001482958,0.0003023251,0.000002029212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001515143,"about_ca_system_score_gemma":0.00006434341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002364608,"about_ca_topic_score_gemma":0.00001545829,"domain_scores_codex":[0.9983374,0.00003583212,0.0003371484,0.0004587566,0.0003704909,0.000460344],"domain_scores_gemma":[0.9993528,0.00004716404,0.00007459272,0.0002554176,0.00009856015,0.0001714359],"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":[0.00006313792,0.000006609854,0.0000968836,0.001070227,0.00004162488,0.00004765251,0.001116336,0.9555481,0.004560285,0.005353815,0.0000576869,0.0320376],"study_design_scores_gemma":[0.000200777,0.00005983942,0.0001434792,0.0003667904,0.00001813661,0.00004459008,0.0006590167,0.9966477,0.0003254979,0.001269768,0.000006182253,0.0002582242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7471752,0.0005024347,0.2495973,0.00006430699,0.0002127287,0.000498455,0.00004088778,0.001706325,0.0002023348],"genre_scores_gemma":[0.9908642,0.000005349638,0.008716156,0.000008162522,0.0002907845,0.00003167359,0.00001480174,0.00005764499,0.00001128231],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2436889,"threshold_uncertainty_score":0.7820358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03536260311921343,"score_gpt":0.2922829988566084,"score_spread":0.256920395737395,"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."}}