{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001575493,0.0007658128,0.0005587068,0.002789711,0.0005581061,0.001039483,0.0009865415,0.001173116,0.009838083],"category_scores_gemma":[0.002969106,0.000479927,0.0004230613,0.003267193,0.0007800828,0.0009893446,0.001371269,0.000965605,0.003277735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007222104,"about_ca_system_score_gemma":0.0006860256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009904408,"about_ca_topic_score_gemma":0.001464726,"domain_scores_codex":[0.9977063,0.0003193162,0.0001002361,0.0003282891,0.001448797,0.00009710521],"domain_scores_gemma":[0.9974571,0.0008801763,0.0002500931,0.0003591326,0.0009769705,0.00007660454],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000228589,0.0001410246,0.001837352,0.001359654,0.00004177506,0.0007806433,0.0004869487,0.005759769,0.1839553,0.02221282,0.02055104,0.762645],"study_design_scores_gemma":[0.00005792538,0.0005667026,0.00606834,0.0004875697,0.0001185415,0.006968057,0.0003590443,0.07428841,0.4575227,0.01399674,0.4393589,0.0002071292],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01188072,0.009968575,0.9286755,0.0006959395,0.0004301562,0.0003810764,0.0003466495,0.003844279,0.04377715],"genre_scores_gemma":[0.1486285,0.0104184,0.8124756,0.000822024,0.0004149548,0.0004167341,0.0004799801,0.0003689718,0.02597482],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009838083,"threshold_uncertainty_score":0.03291166,"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."}}