{"id":"W4389486906","doi":"10.3390/app132413093","title":"A Dataset of Pulsed Thermography for Automated Defect Depth Estimation","year":2023,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Thermography and Photoacoustic Techniques","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"European Regional Development Fund","keywords":"Thermography; Artificial intelligence; Computer science; Dimension (graph theory); Domain (mathematical analysis); CAD; Pattern recognition (psychology); Computer vision; Infrared; Engineering drawing; Mathematics; Engineering; Optics","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":[0.0004241562,0.001218399,0.000711089,0.001962925,0.0004434602,0.0006628468,0.001222613,0.001613893,0.003600155],"category_scores_gemma":[0.001320497,0.0003041597,0.0009448968,0.001668611,0.0004283097,0.0005087737,0.0007495725,0.00104424,0.004378772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005319836,"about_ca_system_score_gemma":0.0009504493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005463727,"about_ca_topic_score_gemma":0.01441005,"domain_scores_codex":[0.9994094,0.00004242376,0.00005168482,0.0002010957,0.0002305433,0.00006476702],"domain_scores_gemma":[0.9991423,0.0001434758,0.00008772441,0.0002141644,0.0003514898,0.00006082197],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001472586,0.001681038,0.04302104,0.005229901,0.0004570578,0.003163431,0.0004086698,0.05370424,0.201152,0.002521821,0.3407213,0.3464668],"study_design_scores_gemma":[0.0002855196,0.001189602,0.1917009,0.0007755886,0.0003279134,0.005210426,0.0007926579,0.1444431,0.1841655,0.004661378,0.466029,0.0004184597],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.2003971,0.003884164,0.06906522,0.0005538936,0.0006253007,0.0005342055,0.7059868,0.01077359,0.008179715],"genre_scores_gemma":[0.173472,0.001030781,0.06266511,0.0002038152,0.00008996294,0.0008147212,0.7556677,0.0004781492,0.005577771],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.005463727,"threshold_uncertainty_score":0.01204371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02186137128062448,"score_gpt":0.2792656585509211,"score_spread":0.2574042872702966,"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."}}