{"id":"W4403028417","doi":"10.3390/a17100439","title":"Methods for Corrosion Detection in Pipes Using Thermography: A Case Study on Synthetic Datasets","year":2024,"lang":"en","type":"article","venue":"Algorithms","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Université Laval","keywords":"Thermography; Corrosion; Computer science; Data mining; Artificial intelligence; Materials science; Remote sensing; Geology; Metallurgy; Physics; Optics; Infrared","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":[0.002755547,0.0008077848,0.0005969836,0.001350388,0.0003411884,0.0007536515,0.0008379435,0.00129614,0.0003292239],"category_scores_gemma":[0.008970838,0.0002593182,0.0008033495,0.00133792,0.0006614163,0.0008314133,0.0005531639,0.0007694158,0.0001740073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000605344,"about_ca_system_score_gemma":0.0004221872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003183956,"about_ca_topic_score_gemma":0.004784145,"domain_scores_codex":[0.9984351,0.0006842923,0.0001488671,0.0002267971,0.0004414029,0.00006350712],"domain_scores_gemma":[0.9919412,0.005283024,0.0006413349,0.0008081746,0.001215037,0.0001111778],"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":[0.0006004762,0.0006355619,0.04360145,0.001386944,0.0002713372,0.001243771,0.0005524544,0.7514151,0.01964103,0.004262916,0.01006663,0.1663222],"study_design_scores_gemma":[0.00004073999,0.0003329628,0.0165778,0.00008699823,0.00004172716,0.0007531364,0.0003680027,0.9529825,0.01936072,0.002628787,0.006779049,0.00004750322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.735732,0.002617479,0.2510537,0.001553617,0.0001890401,0.0003463684,0.003976477,0.001384649,0.00314685],"genre_scores_gemma":[0.8572449,0.0008126845,0.1364501,0.0001086745,0.00007143045,0.0001636452,0.004215784,0.00009219181,0.0008405394],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003183956,"threshold_uncertainty_score":0.01457292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0375859643272865,"score_gpt":0.3614692335370241,"score_spread":0.3238832692097376,"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."}}