{"id":"W4306176445","doi":"10.1080/17686733.2022.2126638","title":"Multi-label classification algorithms for composite materials under infrared thermography testing","year":2022,"lang":"en","type":"article","venue":"Quantitative InfraRed Thermography Journal","topic":"Thermography and Photoacoustic Techniques","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Université Laval","funders":"","keywords":"Thermography; Benchmark (surveying); Computer science; Infrared; Algorithm; Perspective (graphical); Composite number; Random forest; Work (physics); Key (lock); Nondestructive testing; Artificial intelligence; Machine learning; Pattern recognition (psychology); Data mining; Optics; Mechanical engineering; Engineering; Geology","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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.001198527,0.0005665022,0.0005800988,0.001305781,0.001347608,0.0002980185,0.0007615425,0.0001696392,0.0002874758],"category_scores_gemma":[0.00005805966,0.0005698048,0.0004478672,0.00188207,0.0002522257,0.000431528,0.00008522553,0.0008629729,0.000002039883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001160255,"about_ca_system_score_gemma":0.00006232443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001307809,"about_ca_topic_score_gemma":0.000001114243,"domain_scores_codex":[0.9968462,0.0004429127,0.0009884846,0.0004396031,0.0005294186,0.0007534024],"domain_scores_gemma":[0.997781,0.0006890943,0.0004967709,0.0004235922,0.0003894758,0.0002200445],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003458577,0.0003848388,0.001032757,0.0001058013,0.001084387,0.00002041642,0.002393031,0.002355805,0.973883,0.002957741,0.001817836,0.01361848],"study_design_scores_gemma":[0.01985795,0.007298729,0.2030606,0.0009420245,0.001575731,0.001241604,0.02761717,0.306774,0.129715,0.2822863,0.01171946,0.007911456],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5203453,0.003327237,0.4631496,0.00006302419,0.001982235,0.00192161,0.005613795,0.001727455,0.001869757],"genre_scores_gemma":[0.7860212,0.0001531914,0.2119589,0.0002385287,0.0001984957,0.0008966943,0.0002382543,0.0002403849,0.00005431333],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8441681,"threshold_uncertainty_score":0.9999525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09017141866185227,"score_gpt":0.3063976859501761,"score_spread":0.2162262672883239,"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."}}