{"id":"W2772705348","doi":"10.1111/str.12258","title":"Time‐resolved identification of mechanical loadings on plates using the virtual fields method and deflectometry measurements","year":2017,"lang":"en","type":"article","venue":"Strain","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Shaker; Transient (computer programming); Time domain; Virtual work; Hammer; Frequency domain; Field (mathematics); Work (physics); Identification (biology); Magnitude (astronomy); Domain (mathematical analysis); Mechanics; Structural engineering; Acoustics; Materials science; Computer science; Physics; Engineering; Mathematical analysis; Mathematics; Vibration; Finite element method; Mechanical engineering; Computer vision","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":[0.0005432762,0.00008754359,0.0001176446,0.00005311941,0.0001572736,0.00004325739,0.0002106544,0.00009034848,0.000008666141],"category_scores_gemma":[0.0001850022,0.00006977329,0.00002499963,0.00004117886,0.00004345994,0.00007425596,0.0000324573,0.0001477933,0.000001295614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004255921,"about_ca_system_score_gemma":0.000007464097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003406879,"about_ca_topic_score_gemma":0.000002516858,"domain_scores_codex":[0.9993677,0.00003359693,0.0001896583,0.0001129929,0.0001640806,0.0001319782],"domain_scores_gemma":[0.9994594,0.0001011177,0.00008101411,0.0002960181,0.00002870515,0.00003374863],"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":[0.00002015461,0.000005405276,0.0002239514,0.00006388758,0.00004319705,7.985143e-7,0.000328091,0.0006012826,0.9179492,0.0006039911,0.0000799465,0.0800801],"study_design_scores_gemma":[0.0002031648,0.00008785645,0.02064569,0.0001026072,0.00002504309,0.000005422066,0.00004610881,0.04889164,0.9256713,0.004153003,0.00002437181,0.0001438308],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9819431,0.00003517617,0.01727248,0.00006164147,0.0002541028,0.0001584593,0.00001165176,0.0001075576,0.0001557769],"genre_scores_gemma":[0.9938287,0.000006154573,0.00606018,0.00001054365,0.00006479058,0.00000453748,0.00000104578,0.00001338939,0.0000107301],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07993627,"threshold_uncertainty_score":0.2845272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07822736008085142,"score_gpt":0.3755084514301207,"score_spread":0.2972810913492693,"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."}}