{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004113238,0.0004020732,0.0005581018,0.0001262561,0.0001480809,0.00005955846,0.0002231011,0.000231048,0.00007492177],"category_scores_gemma":[0.00002079931,0.0004343069,0.000213734,0.0004187074,0.00007557622,0.00009318515,0.00001676789,0.0003348217,0.00006520845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004200547,"about_ca_system_score_gemma":0.00006709568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002614211,"about_ca_topic_score_gemma":0.00005096371,"domain_scores_codex":[0.9975199,0.0001592646,0.0008717955,0.0004506096,0.0004088581,0.0005896327],"domain_scores_gemma":[0.9988636,0.0001906372,0.0001210456,0.0003908151,0.0002576716,0.0001762483],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001003345,0.0001040155,0.02613661,0.003468724,0.0007532479,0.001030713,0.0008745189,0.8674635,0.09131569,0.006142089,0.0005006589,0.002109852],"study_design_scores_gemma":[0.0009965219,0.0002839525,0.01512307,0.009277685,0.0008816918,0.007404087,0.001070308,0.8748347,0.06929436,0.01564077,0.001065399,0.00412745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8217046,0.001395122,0.1666204,0.000006326041,0.001779754,0.0006428625,0.0001226633,0.002950764,0.00477754],"genre_scores_gemma":[0.948409,0.00004687883,0.05037366,0.000009324005,0.0006769562,0.00007980435,0.0002641333,0.0001191564,0.00002112539],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1267044,"threshold_uncertainty_score":0.9998109,"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."}}