{"id":"W3153641163","doi":"10.3390/s21082682","title":"Robust Principal Component Thermography for Defect Detection in Composites","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Thermography and Photoacoustic Techniques","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Jaccard index; Principal component analysis; Robust principal component analysis; Outlier; Thermography; Pattern recognition (psychology); Noise (video); Computer science; Sparse PCA; Artificial intelligence; Materials science; Optics; Physics","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":[0.00009004505,0.0001208912,0.0001421866,0.0001381031,0.00004023859,0.00001809766,0.00005594626,0.00008014182,0.0000139708],"category_scores_gemma":[0.000008628805,0.0001271167,0.0001658474,0.0002864743,0.00002263272,0.00002513113,0.000009586915,0.0001217058,8.317166e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002418901,"about_ca_system_score_gemma":0.000004273396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001960261,"about_ca_topic_score_gemma":0.000137273,"domain_scores_codex":[0.9994097,0.00003201019,0.0001491625,0.0001460385,0.00006639785,0.0001966944],"domain_scores_gemma":[0.9996696,0.0001008574,0.00001567681,0.0001493637,0.0000277493,0.00003678985],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005781697,0.00008431739,0.0027524,0.0001908113,0.0001289771,0.00003221087,0.0006630151,0.1413809,0.8429148,0.000215289,0.00005510151,0.01152432],"study_design_scores_gemma":[0.0005661812,0.00007381523,0.03206014,0.00008226534,0.00004584935,0.0000310143,0.000194317,0.1020892,0.8616529,0.0006726465,0.00214849,0.000383249],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9820277,0.0003527177,0.01587749,0.000007609314,0.0001828295,0.000244786,0.00006966161,0.0004603963,0.0007768053],"genre_scores_gemma":[0.9974908,0.00004080309,0.002310134,0.00001567026,0.00004003204,0.00004782017,0.00001696034,0.00002862924,0.000009175767],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03929169,"threshold_uncertainty_score":0.518367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01471883266157645,"score_gpt":0.2069605045675136,"score_spread":0.1922416719059372,"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."}}