{"id":"W3128958978","doi":"10.1016/j.ultras.2021.106372","title":"Supervised learning strategy for classification and regression tasks applied to aeronautical structural health monitoring problems","year":2021,"lang":"en","type":"article","venue":"Ultrasonics","topic":"Ultrasonics and Acoustic Wave Propagation","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":false,"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"Structural health monitoring; Computer science; Kernel (algebra); Artificial intelligence; Machine learning; Regression; Process (computing); Kriging; Inversion (geology); Regression analysis; Pattern recognition (psychology); Data mining; Engineering; Mathematics; Statistics; Structural engineering","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":[0.002660331,0.0008607265,0.001384155,0.0008027393,0.0006363016,0.0008929363,0.001828682,0.001650747,0.002716964],"category_scores_gemma":[0.005587338,0.0004570723,0.0008691691,0.0007518156,0.0005736421,0.001116433,0.001097283,0.001574998,0.001132473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007151261,"about_ca_system_score_gemma":0.00172291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006359266,"about_ca_topic_score_gemma":0.006554313,"domain_scores_codex":[0.9988451,0.0004855507,0.0001143147,0.0002505183,0.0001997548,0.0001047549],"domain_scores_gemma":[0.9969718,0.001550586,0.0001498754,0.0002344454,0.0009975893,0.00009566099],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003372502,0.000635971,0.001526736,0.0002189128,0.0001969821,0.0001515981,0.0001392101,0.4448069,0.01004618,0.01093763,0.007217277,0.5237853],"study_design_scores_gemma":[0.000008175626,0.00002623936,0.00007903926,0.000002874634,0.000005468046,0.000007071988,0.000004376765,0.9975591,0.0005955106,0.00151657,0.0001932292,0.000002362243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01391622,0.000252013,0.9842514,0.0001887863,0.00004787735,0.00007151414,0.0000657925,0.0005938931,0.0006124824],"genre_scores_gemma":[0.4933813,0.0003565104,0.4964786,0.000370753,0.0002553726,0.000613065,0.001009366,0.0002513805,0.007283647],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006359266,"threshold_uncertainty_score":0.01406938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03530498938212443,"score_gpt":0.2785059366122452,"score_spread":0.2432009472301207,"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."}}