{"id":"W4406095416","doi":"10.1007/s13349-024-00905-7","title":"Damage identification method of arch bridges using MobileViT and transfer learning","year":2025,"lang":"en","type":"article","venue":"Journal of Civil Structural Health Monitoring","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Arch; Identification (biology); Structural engineering; Engineering; Computer science; Biology","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":[0.0002769449,0.0004576125,0.0005591761,0.000799616,0.0004047148,0.0003487676,0.0006648647,0.0007507625,0.001655467],"category_scores_gemma":[0.0006617741,0.0002204653,0.0005196686,0.0004633375,0.0002497066,0.0008722504,0.0004996046,0.0004912147,0.000343291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000247606,"about_ca_system_score_gemma":0.0004086165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002267859,"about_ca_topic_score_gemma":0.002238697,"domain_scores_codex":[0.9998241,0.00001947656,0.000009121607,0.00006628099,0.00005326142,0.00002774431],"domain_scores_gemma":[0.9997196,0.0000874414,0.00002636688,0.00002979166,0.0001177795,0.00001910773],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000364743,0.000216106,0.006267414,0.0001218137,0.00008339498,0.0002392271,0.0001899465,0.2277816,0.04769005,0.002675546,0.001524236,0.712846],"study_design_scores_gemma":[0.000006189185,0.00006771618,0.001930796,0.000003678821,0.00001327606,0.00006735795,0.00002065146,0.9939712,0.002898891,0.0007568388,0.0002553843,0.000008056237],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1366759,0.0002639388,0.8608598,0.00008149771,0.00005650416,0.00003278185,0.00003744919,0.0004159891,0.001576168],"genre_scores_gemma":[0.9169232,0.0001621946,0.07861748,0.0000508318,0.00005016366,0.00005840251,0.000121464,0.00002650957,0.003989748],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002267859,"threshold_uncertainty_score":0.005538046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03056840017812663,"score_gpt":0.3772613365309964,"score_spread":0.3466929363528698,"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."}}