{"id":"W2574621582","doi":"10.1680/jbren.15.00033","title":"Bridge weigh-in-motion using fibre optic sensors","year":2017,"lang":"en","type":"article","venue":"Proceedings of the Institution of Civil Engineers - Bridge Engineering","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Queen's University; Queen's University Belfast; Science Foundation Ireland; Invest Northern Ireland; National Science Foundation","keywords":"Weigh in motion; Bridge (graph theory); Axle; Structural health monitoring; Strain gauge; Engineering; Calibration; Span (engineering); Structural engineering; Automotive engineering; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002597816,0.0003827021,0.0004973321,0.0003599848,0.00009054305,0.00004058101,0.0006883987,0.0001847339,0.000009917751],"category_scores_gemma":[0.0005064144,0.000388156,0.0001928862,0.0002836647,0.0001709603,0.0009446729,0.0001320874,0.0004204445,0.000003162115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004751182,"about_ca_system_score_gemma":0.00003543595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005493376,"about_ca_topic_score_gemma":0.000006262938,"domain_scores_codex":[0.9981975,0.00000276089,0.0006435828,0.000283846,0.0003972178,0.0004751094],"domain_scores_gemma":[0.9990117,0.00004124558,0.0002662833,0.0004131904,0.0001666672,0.0001009392],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009204835,0.00001838822,0.0009689031,0.0006029972,0.00005404301,0.000003135855,0.0002755965,0.9194247,0.07452045,0.003901972,0.00006885656,0.0001518144],"study_design_scores_gemma":[0.0006892534,0.00002046178,0.0465109,0.0009582368,0.00005954208,0.0000769687,0.00003222642,0.857076,0.09370532,0.00004823495,0.0003641198,0.0004587323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9829879,0.0002763483,0.0121498,0.0000443208,0.001516621,0.0003588117,0.00001468044,0.0002329602,0.002418579],"genre_scores_gemma":[0.9953718,0.00006218278,0.004288203,0.000002958255,0.0001494935,0.00001090269,0.000001712834,0.00008156325,0.00003120349],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06234863,"threshold_uncertainty_score":0.999857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01676363941289875,"score_gpt":0.2271051942361144,"score_spread":0.2103415548232156,"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."}}