{"id":"W4318214958","doi":"10.20944/preprints202301.0483.v1","title":"Pulsed Thermography Dataset for Training Deep Learning Models","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Thermography and Photoacoustic Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"European Regional Development Fund","keywords":"Thermography; Deep learning; Preprocessor; Artificial intelligence; Segmentation; Computer science; Field (mathematics); Data pre-processing; Market segmentation; Pattern recognition (psychology); Image segmentation; Machine learning; Data mining","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":[0.0005636539,0.001169327,0.0006255854,0.001374845,0.0003728886,0.000792737,0.001609507,0.001660696,0.01219549],"category_scores_gemma":[0.001871715,0.000323311,0.0008622495,0.001413318,0.0004182431,0.0006109777,0.0007693949,0.001982816,0.009987039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000670232,"about_ca_system_score_gemma":0.0009298202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003952737,"about_ca_topic_score_gemma":0.009495077,"domain_scores_codex":[0.9995702,0.00005280078,0.00004470486,0.0001201806,0.0001579446,0.00005419],"domain_scores_gemma":[0.9992149,0.0001867427,0.00006113036,0.0002861306,0.0002016236,0.00004945245],"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":[0.001103989,0.0009636763,0.007073688,0.001981371,0.0002166907,0.0007948768,0.0001535782,0.05316299,0.0430009,0.006453117,0.6856219,0.1994733],"study_design_scores_gemma":[0.0004352791,0.0006386319,0.02051951,0.0002904946,0.0001348873,0.001773894,0.0002563029,0.2368645,0.1039755,0.01387346,0.6209777,0.0002599207],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.08750286,0.003338042,0.1556124,0.001839347,0.001618769,0.0008253704,0.6852903,0.04463458,0.01933838],"genre_scores_gemma":[0.1250456,0.001387834,0.09342341,0.0006093595,0.0001756828,0.001328882,0.7620574,0.002098295,0.01387346],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01219549,"threshold_uncertainty_score":0.04079801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2206849506113399,"score_gpt":0.3302805733537706,"score_spread":0.1095956227424307,"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."}}