{"id":"W2804806906","doi":"10.1109/tii.2018.2836363","title":"Automated Dynamic Inspection Using Active Infrared Thermography","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Thermography and Photoacoustic Techniques","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Government of Canada; Université Laval","funders":"","keywords":"Thermography; Robustness (evolution); Computer science; Infrared; Automated X-ray inspection; Artificial intelligence; Computer vision; Engineering; Image processing; Image (mathematics)","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":[],"consensus_categories":[],"category_scores_codex":[0.0001077047,0.0002415128,0.0002017991,0.000566159,0.0002820651,0.00005775273,0.0001551401,0.0003744733,0.00009682825],"category_scores_gemma":[0.000003431289,0.0002447818,0.0001363707,0.0009598348,0.0001602855,0.0004429828,9.753261e-7,0.0005367632,0.00001357973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001473717,"about_ca_system_score_gemma":0.00003796083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003116558,"about_ca_topic_score_gemma":0.0000116464,"domain_scores_codex":[0.9989528,0.00002805153,0.0004616663,0.00008620372,0.0001909344,0.0002803373],"domain_scores_gemma":[0.9994229,0.00005243817,0.00008860084,0.000268082,0.00007956784,0.00008843224],"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":[0.001375543,0.0007079074,0.00003784963,0.0002871357,0.002677232,0.000009883959,0.0303075,0.5021009,0.09289888,0.0001445871,0.008092186,0.3613603],"study_design_scores_gemma":[0.0007831363,0.0002976237,0.00004733976,0.0001140568,0.0001062355,0.00001770295,0.0007978565,0.8196424,0.1769611,0.0001493507,0.0006840051,0.0003991549],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4545985,0.000004369014,0.5345005,0.000003421093,0.001827505,0.0004152604,0.0004392801,0.005322537,0.002888617],"genre_scores_gemma":[0.9983563,0.0000207788,0.001385451,0.00003718934,0.000107869,0.00003146344,0.000008151002,0.00003897156,0.00001375486],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5437579,"threshold_uncertainty_score":0.9981909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02459034955287728,"score_gpt":0.2522301247435627,"score_spread":0.2276397751906854,"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."}}