{"id":"W2299951517","doi":"10.1680/jgeen.15.00171","title":"Measurement of rail deflection on soft subgrades using DIC","year":2016,"lang":"en","type":"article","venue":"Proceedings of the Institution of Civil Engineers - Geotechnical Engineering","topic":"Railway Engineering and Dynamics","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Track (disk drive); Displacement (psychology); Train; High-speed camera; Digital camera; Digital image correlation; Computer vision; Deflection (physics); Computer science; Artificial intelligence; Vibration; Video camera; Acoustics; Measure (data warehouse); Optics; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004384284,0.0003257432,0.0004472355,0.0003810392,0.00003876685,0.00000943643,0.000406761,0.0002256033,0.000004232317],"category_scores_gemma":[0.000545904,0.0002409585,0.0002501857,0.0006710376,0.0001304798,0.000235529,0.00006102155,0.0002808626,9.789568e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003403002,"about_ca_system_score_gemma":0.00004004035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008646645,"about_ca_topic_score_gemma":0.00000197071,"domain_scores_codex":[0.9980658,0.000003149264,0.0006822363,0.000217219,0.0006830659,0.0003485836],"domain_scores_gemma":[0.9991962,0.00005364418,0.0001584371,0.0002314188,0.0002672632,0.00009303248],"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":[0.00001194368,0.00002414742,0.00005992343,0.000401029,0.00005281086,1.279192e-7,0.00001885698,0.6048159,0.3886491,0.005541325,0.00001612787,0.0004087412],"study_design_scores_gemma":[0.0006108452,0.0001157496,0.001428469,0.002338869,0.00008382612,0.00001749606,0.00001321508,0.5700201,0.4244009,0.0001513527,0.0004425252,0.0003766592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6215548,0.0004371299,0.3753223,0.00006279381,0.0009245746,0.0003682871,0.00002263697,0.0006758019,0.0006317098],"genre_scores_gemma":[0.998357,0.00008395933,0.001408942,0.000002465029,0.00006572683,0.00001559736,4.129411e-7,0.00005901867,0.000006899019],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3768022,"threshold_uncertainty_score":0.9826002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01297791372154309,"score_gpt":0.1959995839852729,"score_spread":0.1830216702637298,"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."}}