{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003006711,0.000479653,0.0004301639,0.00104365,0.0001674949,0.0006641453,0.0007074576,0.0005567215,0.0010779],"category_scores_gemma":[0.0007902801,0.0003283782,0.0003382445,0.0005647735,0.0003308617,0.000803783,0.0005215095,0.0003712219,0.0005374633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002444807,"about_ca_system_score_gemma":0.0002944267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007707212,"about_ca_topic_score_gemma":0.0007831223,"domain_scores_codex":[0.9994802,0.00006308885,0.00001429462,0.0001118376,0.0002918413,0.00003884887],"domain_scores_gemma":[0.9993556,0.0001985953,0.0001264461,0.0001373266,0.0001633549,0.00001875044],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003124837,0.0001063591,0.003057282,0.0002661815,0.00003701178,0.0001689354,0.0001435907,0.04166251,0.5124586,0.001881766,0.001314986,0.4385902],"study_design_scores_gemma":[0.00002458869,0.0003722619,0.009292902,0.00004230279,0.00004066699,0.0006148246,0.00006898844,0.7535508,0.2280013,0.00141262,0.006505216,0.00007337005],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08807169,0.0008263166,0.9050081,0.00006131572,0.00004040026,0.0000601409,0.00007039093,0.00256272,0.003298963],"genre_scores_gemma":[0.765452,0.000456684,0.2310755,0.00004679782,0.00003398778,0.00005359015,0.0001444191,0.0001457793,0.002591264],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0010779,"threshold_uncertainty_score":0.003605902,"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."}}