{"id":"W4400147614","doi":"10.58286/29589","title":"Toward Indirect Real-Time Prediction of Bridge Vibration Responses Under Traffic Flow Through a Population of Connected Sensing Vehicles","year":2024,"lang":"en","type":"article","venue":"e-Journal of Nondestructive Testing","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Traverse; Bridge (graph theory); Timestamp; Acceleration; Computer science; Modal; Population; Real-time computing; Traffic flow (computer networking); Engineering; Simulation; Computer network","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":[0.0004396742,0.0001782956,0.0003788594,0.0003528704,0.00005949421,0.00003019119,0.00009030526,0.000126522,0.000006235946],"category_scores_gemma":[0.0005557272,0.0001736747,0.00008538335,0.0006351809,0.00005608789,0.0005836238,0.00001741892,0.0003131815,6.068311e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003054245,"about_ca_system_score_gemma":0.0001039594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000105955,"about_ca_topic_score_gemma":0.00000165127,"domain_scores_codex":[0.9983628,0.0001334206,0.0008462782,0.0001457015,0.0003275528,0.0001842856],"domain_scores_gemma":[0.9982916,0.0008321204,0.0003583902,0.0001130691,0.000356541,0.00004825742],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0003628277,0.00002235204,0.007708876,0.001505376,0.000311781,0.00006706826,0.005610902,0.1099069,0.7146236,0.0001420255,0.0001253804,0.159613],"study_design_scores_gemma":[0.0002868437,0.0003765881,0.7188876,0.002207313,0.0001006076,0.000652145,0.0001533324,0.2036396,0.06882075,0.004700523,0.000001988664,0.0001726309],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930604,0.0002715929,0.005289298,0.00002170391,0.0006338907,0.0001680316,0.00003323731,0.0004429103,0.00007890919],"genre_scores_gemma":[0.8616157,0.0000280836,0.1380081,0.000001718436,0.0002965475,9.244429e-7,0.000006987547,0.00004033179,0.00000166199],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7111788,"threshold_uncertainty_score":0.7082248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06575633926614394,"score_gpt":0.3042766026807401,"score_spread":0.2385202634145961,"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."}}