{"id":"W2955823667","doi":"10.3390/app9132719","title":"Digital Image Correlation Applications in Composite Automated Manufacturing, Inspection, and Testing","year":2019,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Digital image correlation; Composite number; Automated X-ray inspection; Materials science; Computer science; Mechanical engineering; Structural engineering; Composite material; Engineering; Image processing; Artificial intelligence; Image (mathematics)","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":[0.0002133244,0.00008129335,0.00008890859,0.0001325451,0.0001409843,0.0004356345,0.0003512187,0.00002924197,0.000003403694],"category_scores_gemma":[0.00001229872,0.00006786771,0.000008993365,0.0004375808,0.0001388323,0.0008125133,0.0001183076,0.00008046165,0.00005139396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002494026,"about_ca_system_score_gemma":0.00001876136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009642879,"about_ca_topic_score_gemma":0.000002470061,"domain_scores_codex":[0.9991791,0.000007171911,0.0001528843,0.0003142753,0.0001888098,0.0001577076],"domain_scores_gemma":[0.9996552,0.00008840052,0.00005601747,0.0001371722,0.00002773549,0.00003546907],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007979495,0.0002393196,0.3645642,0.0000536761,0.000009219522,0.00000145504,0.0009519281,0.0004162192,0.2124239,0.3595718,0.0002373879,0.06152297],"study_design_scores_gemma":[0.0004299906,0.0002970956,0.3894056,0.00009094325,0.000004277715,0.00001092837,0.0003209775,0.4472302,0.1329259,0.02832652,0.0003793449,0.000578295],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7128989,0.0000127862,0.07537795,0.0001206112,0.00005568966,0.0004622972,6.756657e-7,0.001261474,0.2098096],"genre_scores_gemma":[0.9753695,9.044452e-7,0.02453621,0.00003158717,0.000007390203,0.00002598146,0.000001107124,0.000002266175,0.00002501621],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4468139,"threshold_uncertainty_score":0.4200834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01930018709153244,"score_gpt":0.2515133303635617,"score_spread":0.2322131432720293,"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."}}