{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005657351,0.0001193171,0.0001229192,0.0002385059,0.0002891121,0.00006294905,0.0003104807,0.00005410731,0.00001682839],"category_scores_gemma":[0.000006682026,0.0001070581,0.0000535119,0.0009785376,0.0001506422,0.0001153855,0.00002118202,0.0001063136,0.000003592149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004342723,"about_ca_system_score_gemma":0.000007916413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000388773,"about_ca_topic_score_gemma":0.000004133508,"domain_scores_codex":[0.9991325,0.000008234518,0.0001286553,0.0002183022,0.0001530151,0.000359296],"domain_scores_gemma":[0.9996314,0.0001698093,0.0000214145,0.0001199945,0.000007483046,0.00004988773],"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":[0.00002022122,0.00001537793,0.0000554547,0.0001020998,0.00007542543,0.000002925498,0.006339817,0.6173851,0.1630856,0.02426602,0.005824995,0.1828269],"study_design_scores_gemma":[0.0002704127,0.00007996707,0.0002029544,0.00002069637,0.00002582322,0.000002017445,0.00322609,0.892914,0.01223838,0.08033878,0.01020912,0.0004717629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3231103,0.0005397168,0.6197109,0.00008683137,0.0006050412,0.001711419,0.003079493,0.008102013,0.0430543],"genre_scores_gemma":[0.997039,0.0000413099,0.002383046,0.00003880934,0.00004201262,0.0002083109,0.0002219282,0.00001903654,0.000006543682],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6739287,"threshold_uncertainty_score":0.4365704,"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."}}