{"id":"W4414400402","doi":"10.1016/j.engstruct.2025.121399","title":"A novel decentralized damage detection method for self-powered wireless sensing in structural health monitoring using self-supervised learning","year":2025,"lang":"en","type":"article","venue":"Engineering Structures","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Science Foundation of Jiangsu Province","keywords":"Robustness (evolution); Structural health monitoring; Wireless sensor network; Benchmark (surveying); Key (lock); Bridge (graph theory); Wireless; Feature extraction","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":[0.0003698452,0.000459071,0.0008480072,0.0004467636,0.0003305439,0.0003842747,0.001144872,0.0006743338,0.0009508373],"category_scores_gemma":[0.001079631,0.0002945072,0.0004529905,0.0003551422,0.000397498,0.0008224146,0.0007450883,0.0005167575,0.0002901869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003396929,"about_ca_system_score_gemma":0.0005279902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001412921,"about_ca_topic_score_gemma":0.002583665,"domain_scores_codex":[0.9996327,0.00004884087,0.00001722078,0.0001288813,0.000136844,0.00003548724],"domain_scores_gemma":[0.9994059,0.0001843625,0.00009851648,0.00008930754,0.0001877927,0.00003418713],"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":[0.0002497561,0.0003519428,0.003183654,0.0001827841,0.0001335943,0.0001908449,0.0001618971,0.3299835,0.06860514,0.006197452,0.003970671,0.5867888],"study_design_scores_gemma":[0.000006234557,0.00003257838,0.0004432205,0.000002108788,0.000007407222,0.00004150396,0.000004900326,0.9957557,0.002722081,0.0007077879,0.0002711239,0.000005343338],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01978469,0.0001222684,0.9785322,0.00006057098,0.00003851797,0.00003235669,0.00002388428,0.0003928893,0.001012598],"genre_scores_gemma":[0.7839429,0.0001431132,0.2109929,0.0001352804,0.0001292037,0.0001371202,0.0001331112,0.00006024459,0.00432607],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001412921,"threshold_uncertainty_score":0.003180861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01369062894464447,"score_gpt":0.318066213123292,"score_spread":0.3043755841786475,"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."}}