{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003176586,0.0002128001,0.0001433805,0.0009962583,0.0001568897,0.000387935,0.0002865766,0.000328625,0.001877349],"category_scores_gemma":[0.000489476,0.0001292831,0.0001254953,0.0003327876,0.000152376,0.000465978,0.0002764435,0.0003087552,0.0006647118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001918128,"about_ca_system_score_gemma":0.0001928574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001388052,"about_ca_topic_score_gemma":0.003510956,"domain_scores_codex":[0.9995399,0.00004748041,0.00001590756,0.000102806,0.0002678424,0.0000260706],"domain_scores_gemma":[0.9996858,0.0000451801,0.00006746835,0.00003493326,0.0001513562,0.0000153819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000246396,0.000275432,0.07608015,0.0001049578,0.00003182912,0.0003464139,0.00059305,0.003432258,0.7360135,0.0006854307,0.003228887,0.1789617],"study_design_scores_gemma":[0.00005105355,0.001964841,0.4271031,0.0001047823,0.00009429723,0.002123211,0.001037373,0.06289384,0.4883836,0.0007985718,0.01534553,0.00009983576],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8591135,0.0005760473,0.1202548,0.0001794959,0.00006005751,0.0001639729,0.000623902,0.0009491429,0.01807914],"genre_scores_gemma":[0.9678179,0.0002688713,0.02615117,0.00007921616,0.00002219881,0.00004232577,0.0002846773,0.00003898095,0.005294747],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001877349,"threshold_uncertainty_score":0.006280303,"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."}}