{"id":"W4406556094","doi":"10.1016/j.autcon.2025.105965","title":"Integration of thermographic inspection data with BIM for enhanced concrete infrastructure assessment","year":2025,"lang":"en","type":"article","venue":"Automation in Construction","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Engineering; Forensic engineering; Construction engineering; Computer science","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":[0.001275733,0.0008262588,0.0005520423,0.003579768,0.0002963394,0.001319167,0.001016208,0.0005099686,0.00197467],"category_scores_gemma":[0.002834521,0.0004704485,0.0007197391,0.001813456,0.0003892838,0.001729182,0.001855553,0.0005356314,0.0007032104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004400186,"about_ca_system_score_gemma":0.0009080964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002980219,"about_ca_topic_score_gemma":0.005110102,"domain_scores_codex":[0.9987663,0.0003256282,0.00007037619,0.000157331,0.000607126,0.00007326136],"domain_scores_gemma":[0.998629,0.0003516816,0.0001902467,0.0003559727,0.0004127998,0.00006036127],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002189287,0.0003345791,0.01278707,0.0005378117,0.0001393157,0.0003950028,0.0009333969,0.1789142,0.06454825,0.01373501,0.003434623,0.7240217],"study_design_scores_gemma":[0.00002168308,0.0001676117,0.01147922,0.0001583591,0.00009107267,0.0004341459,0.0006707441,0.9014688,0.05036365,0.01160565,0.02342874,0.0001102703],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02247185,0.0001469761,0.9712507,0.0001003625,0.00002383186,0.0000980348,0.0004254171,0.002847965,0.002634873],"genre_scores_gemma":[0.3344887,0.0002468914,0.6628063,0.00005056964,0.00002336799,0.0001424212,0.001029728,0.0002560477,0.0009560775],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003579768,"threshold_uncertainty_score":0.006746829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01493892816874389,"score_gpt":0.2714596796865663,"score_spread":0.2565207515178224,"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."}}