{"id":"W6921941471","doi":"10.1016/j.istruc.2025.108286","title":"Time-domain buffeting response prediction of a long-span bridge: A hybrid machine learning framework","year":2025,"lang":"en","type":"article","venue":"Structures","topic":"Fluid Dynamics and Vibration Analysis","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autoencoder; Aeroelasticity; Serviceability (structure); Bridge (graph theory); Extrapolation; Feature learning; Artificial neural network; Interpolation (computer graphics)","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":[0.0005697094,0.0005685634,0.0004435016,0.0004529719,0.0002237875,0.000528785,0.0009362837,0.001062232,0.0009458042],"category_scores_gemma":[0.0008837538,0.000306808,0.000574804,0.0003784616,0.0003491715,0.0005987655,0.0005478872,0.0009510426,0.0002075424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004259723,"about_ca_system_score_gemma":0.0006076252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008152774,"about_ca_topic_score_gemma":0.006367829,"domain_scores_codex":[0.9998643,0.00003070828,0.000007485334,0.00004653322,0.00002721849,0.00002367979],"domain_scores_gemma":[0.9997254,0.0001363916,0.00003047379,0.00001501157,0.00007455886,0.00001811884],"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":[0.00003079968,0.00003654742,0.00104125,0.00001806222,0.00002787656,0.00004582326,0.00002116369,0.9703765,0.001377454,0.001423216,0.0002952432,0.0253061],"study_design_scores_gemma":[5.474127e-7,0.00000407128,0.00006647222,9.175134e-7,0.000001840564,0.000001743504,0.00000104024,0.99962,0.00006672927,0.0002100011,0.00002563265,0.000001001886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1357044,0.0008093283,0.8598754,0.000441961,0.00005931523,0.00003629036,0.000157111,0.0006451881,0.002271022],"genre_scores_gemma":[0.9531809,0.0003223071,0.04302058,0.000132816,0.00005242207,0.00009055501,0.000287524,0.00002748209,0.002885462],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008152774,"threshold_uncertainty_score":0.01621068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003449568050720326,"score_gpt":0.2154088421003386,"score_spread":0.2119592740496182,"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."}}