{"id":"W4321789740","doi":"10.3390/app13052901","title":"Pulsed Thermography Dataset for Training Deep Learning Models","year":2023,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Thermography and Photoacoustic Techniques","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Deep learning; Thermography; Artificial intelligence; Preprocessor; Computer science; Segmentation; Machine learning; Data pre-processing; Field (mathematics); Process (computing); Pattern recognition (psychology); Infrared","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":[0.0009217458,0.001824093,0.0007521818,0.001548851,0.0005015877,0.0009166168,0.002376044,0.001920055,0.01073994],"category_scores_gemma":[0.002566841,0.0003928156,0.001171754,0.001655846,0.000494144,0.0008490769,0.0009526659,0.002548443,0.009462654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001216847,"about_ca_system_score_gemma":0.001248615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008657075,"about_ca_topic_score_gemma":0.02167041,"domain_scores_codex":[0.9993758,0.00008361699,0.00005954985,0.0001698159,0.0002326804,0.000078538],"domain_scores_gemma":[0.9991723,0.0002050193,0.0000740276,0.0002370884,0.0002583182,0.00005311221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008757834,0.00115607,0.005585908,0.001640626,0.0002632692,0.0005443504,0.0001125807,0.063846,0.02119514,0.004857001,0.7229034,0.1770199],"study_design_scores_gemma":[0.0006353835,0.0007756739,0.01475125,0.0003440878,0.0001555354,0.001196827,0.0002572678,0.4004202,0.06760552,0.01339411,0.5001999,0.0002641558],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.08339614,0.003855689,0.1167619,0.002327422,0.001629484,0.001184883,0.7301143,0.04324681,0.01748325],"genre_scores_gemma":[0.08444502,0.001102528,0.09924041,0.0006927552,0.0001301266,0.001434828,0.8005356,0.001478757,0.01093992],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01073994,"threshold_uncertainty_score":0.03592873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06150658131897701,"score_gpt":0.2581299149224241,"score_spread":0.1966233336034471,"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."}}