{"id":"W2142139938","doi":"10.1115/1.4028786","title":"Damage Identification in Collocated Structural Systems Using Structural Markov Parameters","year":2014,"lang":"en","type":"article","venue":"Journal of Dynamic Systems Measurement and Control","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Alberta","keywords":"Markov chain; Identification (biology); Structural system; Stiffness; Structural health monitoring; Computer science; Algorithm; Structural engineering; Engineering; Machine learning","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001607701,0.00022863,0.0005492801,0.000322196,0.00007683842,0.0001570707,0.0001976671,0.0001221041,6.902129e-7],"category_scores_gemma":[0.00009779689,0.0001932867,0.00006266365,0.0001725748,0.00003133581,0.0002746762,0.000009731605,0.0002727356,3.295553e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000827229,"about_ca_system_score_gemma":0.00003838083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002286357,"about_ca_topic_score_gemma":0.00002356105,"domain_scores_codex":[0.9975524,0.0002645948,0.001111293,0.0001485155,0.0006213487,0.0003018385],"domain_scores_gemma":[0.9988192,0.00006934717,0.0004722289,0.0001902612,0.000328072,0.0001209196],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006858542,0.00002440793,0.1333007,0.004925613,0.0009240373,0.00007474768,0.00133396,0.4031666,0.409649,0.0004916583,0.000165635,0.04525772],"study_design_scores_gemma":[0.001370594,0.00007556074,0.1175067,0.0005740616,0.00006402098,0.0001687453,0.0001949558,0.8796104,0.0001521947,0.0000755459,0.00001317838,0.0001940538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9846613,0.002228502,0.008870056,0.00001870517,0.003579679,0.0005499221,0.000005350598,0.00007511782,0.00001138292],"genre_scores_gemma":[0.9993852,0.00002337381,0.0003599811,0.000004400488,0.000179549,0.00001195355,0.000001187066,0.00003014226,0.000004177511],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4764437,"threshold_uncertainty_score":0.7882004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01799584358987127,"score_gpt":0.2517613493927493,"score_spread":0.233765505802878,"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."}}