{"id":"W4410567332","doi":"10.1016/j.tws.2025.113494","title":"Comparison of deep learning techniques for prediction of stress distribution in stiffened panels","year":2025,"lang":"en","type":"article","venue":"Thin-Walled Structures","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Stress (linguistics); Computer science; Structural engineering; Artificial intelligence; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006760319,0.001151949,0.0004497187,0.0007360956,0.0001940168,0.0004437327,0.0007621741,0.000861089,0.001294491],"category_scores_gemma":[0.001242902,0.0003446994,0.0005058593,0.000414416,0.0002505197,0.0007665381,0.0006085978,0.0007989115,0.0002672511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005195039,"about_ca_system_score_gemma":0.0007548771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007293619,"about_ca_topic_score_gemma":0.007583698,"domain_scores_codex":[0.9998117,0.00002935938,0.00001473943,0.00003997783,0.00006894086,0.00003528092],"domain_scores_gemma":[0.9994679,0.0002442239,0.00004901651,0.00004634607,0.0001546673,0.00003789617],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002729506,0.0001380321,0.002714619,0.0001039082,0.00007019195,0.00008750404,0.00003587948,0.8544785,0.007003061,0.0008220883,0.0009983217,0.133275],"study_design_scores_gemma":[0.000002852621,0.00003228428,0.0003824873,0.000005035169,0.000004714126,0.000005688512,0.000005129555,0.9977946,0.001479504,0.0001779791,0.0001061988,0.000003458351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5830038,0.003437115,0.4000128,0.0007268316,0.0002112195,0.00009011525,0.0004926208,0.003030745,0.008994821],"genre_scores_gemma":[0.9331111,0.0009988071,0.06193642,0.0001425629,0.00003105469,0.0000580469,0.0006150391,0.00007541731,0.003031448],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007293619,"threshold_uncertainty_score":0.01450229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01347190683848767,"score_gpt":0.2831064777148888,"score_spread":0.2696345708764011,"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."}}