{"id":"W3134718954","doi":"10.2749/newyork.2019.2444","title":"Bridge Health Monitoring by Infrared Thermography","year":2019,"lang":"en","type":"article","venue":"Report","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nexen (Canada)","funders":"","keywords":"Bridge (graph theory); Thermography; Nondestructive testing; Visual inspection; Computer science; Structural health monitoring; Standardization; Construction engineering; Engineering; Field (mathematics); Forensic engineering; Transport engineering; Structural engineering; Infrared; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001851708,0.0001393866,0.000202037,0.00007784675,0.00004043458,0.00001923059,0.0001215659,0.00007395884,0.0000192784],"category_scores_gemma":[0.000009669856,0.0001408056,0.00005570698,0.000187299,0.00001015251,0.0000991112,0.00002277097,0.0001941565,0.00002323612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000150641,"about_ca_system_score_gemma":0.00003101013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001486275,"about_ca_topic_score_gemma":2.589942e-7,"domain_scores_codex":[0.9989206,0.00001248831,0.0003450988,0.0001869694,0.0002046023,0.0003302822],"domain_scores_gemma":[0.9993123,0.00002034481,0.00007669081,0.000444242,0.0000271851,0.0001192725],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000008611286,0.00001646601,0.8173041,0.0006775398,0.00008169452,0.0001118135,0.0004951555,0.0001285007,0.01229327,0.00005880963,0.05725523,0.1115688],"study_design_scores_gemma":[0.0001374998,0.00006718577,0.916762,0.0001438683,0.000003388666,0.0002112716,0.00001897641,0.0001213678,0.02323416,0.0003618347,0.05862901,0.0003094521],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9897961,0.001085422,0.0001303887,0.00004502649,0.001904668,0.0002975424,0.000005521501,0.001701563,0.005033792],"genre_scores_gemma":[0.997467,0.0002011234,0.001630937,0.00001822179,0.0002320512,0.00002433431,0.00001160216,0.00004763522,0.0003670257],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1112594,"threshold_uncertainty_score":0.5741884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01590701134395804,"score_gpt":0.2925830756213216,"score_spread":0.2766760642773635,"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."}}