{"id":"W2797092537","doi":"10.1139/cgj-2017-0163","title":"Use of fiber optic sensing to measure distributed rail strains and determine rail seat forces under a moving train","year":2018,"lang":"en","type":"article","venue":"Canadian Geotechnical Journal","topic":"Railway Engineering and Dynamics","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vanguard College; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Deflection (physics); Strain gauge; Structural engineering; Vibration; Optical fiber; Load cell; Engineering; Accelerometer; Track (disk drive); Geotechnical engineering; Computer science; Acoustics; Mechanical engineering; Telecommunications; Optics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0002017499,0.00027222,0.0001210329,0.0005463551,0.0002896575,0.0002041827,0.0003193354,0.0002006662,0.0007912872],"category_scores_gemma":[0.0003873408,0.0001574178,0.00009070218,0.0004339778,0.0002450221,0.0002545706,0.0002110621,0.0002002214,0.0001346672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003376361,"about_ca_system_score_gemma":0.0005240503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01551876,"about_ca_topic_score_gemma":0.05222018,"domain_scores_codex":[0.9998279,0.00001233106,0.000006275307,0.00003996514,0.00009025525,0.00002324375],"domain_scores_gemma":[0.999767,0.00005453645,0.00004923858,0.00001806755,0.00009051879,0.00002064986],"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":[0.0001624732,0.0001048425,0.04018662,0.00006677055,0.00001497433,0.00006734573,0.0004193289,0.002380722,0.9009795,0.0002315236,0.0001572453,0.05522865],"study_design_scores_gemma":[0.00003448776,0.001337513,0.3839352,0.00002919528,0.00006647805,0.0005315356,0.0008741087,0.05373562,0.5566997,0.0002835416,0.002388851,0.00008371992],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9734211,0.00007750027,0.02488927,0.0000233582,0.00001186413,0.00003273238,0.0001210117,0.00009041683,0.001332845],"genre_scores_gemma":[0.9729202,0.00009832739,0.02564737,0.00001876559,0.000004189937,0.00002129594,0.00005891774,0.000009965865,0.001220895],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01551876,"threshold_uncertainty_score":0.03085685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01946161484784164,"score_gpt":0.2067224645806995,"score_spread":0.1872608497328579,"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."}}