{"id":"W3193799888","doi":"10.1049/itr2.12103","title":"Real‐time CVSA decals recognition system using deep convolutional neural network architectures","year":2021,"lang":"en","type":"article","venue":"IET Intelligent Transport Systems","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"General Dynamics (Canada); University of Saskatchewan","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Deep learning; Artificial neural network; Pattern recognition (psychology); Time delay neural network; Speech recognition","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002526994,0.00133949,0.0005363495,0.0009620847,0.0002937204,0.0006453795,0.001265344,0.0006635364,0.004499578],"category_scores_gemma":[0.0006048295,0.0003344868,0.000432038,0.0004357894,0.0001921185,0.0007530489,0.0006065467,0.0009031317,0.002413892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009160662,"about_ca_system_score_gemma":0.0009570341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01922868,"about_ca_topic_score_gemma":0.0194611,"domain_scores_codex":[0.999724,0.00001510071,0.00001664091,0.00009549771,0.00008588572,0.00006285924],"domain_scores_gemma":[0.9996688,0.00003874153,0.00003556191,0.00004746408,0.0001730293,0.00003645895],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001736285,0.0007664855,0.008392254,0.0002747969,0.0002007756,0.000809519,0.00008873078,0.1004278,0.1199502,0.001262779,0.05718849,0.7089019],"study_design_scores_gemma":[0.00003364882,0.0001486081,0.002655264,0.00002162279,0.00003201525,0.0001081881,0.00002969546,0.9513713,0.0412078,0.0004809533,0.003873819,0.00003717616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4993587,0.002678521,0.3840942,0.0009348265,0.001722887,0.0003884077,0.006632678,0.08501995,0.01916991],"genre_scores_gemma":[0.8761136,0.0004264842,0.09986197,0.0004694484,0.0001035555,0.0001335378,0.007134476,0.0003585001,0.0153983],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01922868,"threshold_uncertainty_score":0.03823352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03071318742711673,"score_gpt":0.2434306200370652,"score_spread":0.2127174326099485,"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."}}