{"id":"W7114913434","doi":"10.1016/j.engstruct.2025.121904","title":"Automated standardization of bridge inspection data using generative AI","year":2025,"lang":"en","type":"article","venue":"Engineering Structures","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Gina Cody School of Engineering and Computer Science, Concordia University","keywords":"Transformer; Generative grammar; Bridge (graph theory); Standardization; Benchmark (surveying); Baseline (sea); Process (computing); Natural language","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.004059218,0.001671284,0.0008803655,0.003457964,0.0005602759,0.001729259,0.003024697,0.0008853708,0.001749087],"category_scores_gemma":[0.02006936,0.0007282076,0.002079394,0.002240964,0.001289135,0.002949427,0.003257388,0.002273884,0.001254676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001598478,"about_ca_system_score_gemma":0.00238059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01710695,"about_ca_topic_score_gemma":0.02237745,"domain_scores_codex":[0.994889,0.00135279,0.0003758683,0.001848484,0.00133071,0.0002032017],"domain_scores_gemma":[0.986876,0.006612419,0.0008745803,0.003411007,0.002083591,0.0001423515],"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":[0.0002809552,0.0005610728,0.01883062,0.0005231609,0.0002623399,0.0005812921,0.001837631,0.2195072,0.03743759,0.01368618,0.009730064,0.6967618],"study_design_scores_gemma":[0.0000276077,0.00008070702,0.004418671,0.00004374185,0.00004377674,0.0001782331,0.0002451575,0.9517108,0.01828144,0.01744168,0.007463795,0.00006438982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0223946,0.0001278228,0.9629441,0.0001877698,0.00004715688,0.0002627327,0.001121751,0.01110966,0.001804373],"genre_scores_gemma":[0.3502963,0.0001516327,0.6363342,0.0003196904,0.00003652351,0.0006134263,0.009166012,0.0008869095,0.002195348],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01710695,"threshold_uncertainty_score":0.03401476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01108226325915411,"score_gpt":0.2762064424538186,"score_spread":0.2651241791946645,"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."}}