{"id":"W3201828276","doi":"10.1061/jtepbs.0000601","title":"Evaluating Rail Surface Roughness from Axle-Box Acceleration Measurements: Computational Metrology Approach","year":2021,"lang":"en","type":"article","venue":"Journal of Transportation Engineering Part A Systems","topic":"Railway Engineering and Dynamics","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Alberta","funders":"","keywords":"Surface roughness; Axle; Surface finish; Accelerometer; Acceleration; Root mean square; Axle load; Structural engineering; Engineering; Acoustics; Materials science; Mechanical engineering; Computer science; Electrical engineering; Physics; Composite material","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006048895,0.0002596331,0.0005004756,0.0001457833,0.00004902097,0.00009720925,0.000151487,0.0001519555,0.00001681542],"category_scores_gemma":[0.00005528796,0.000281768,0.000159671,0.00035282,0.000009658339,0.0003773173,0.000002552247,0.0003324695,0.000004229474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001695516,"about_ca_system_score_gemma":0.0000866991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001188978,"about_ca_topic_score_gemma":0.000004950352,"domain_scores_codex":[0.9977769,0.00005871724,0.001052819,0.0001901118,0.0006642443,0.0002571989],"domain_scores_gemma":[0.9988415,0.0001395935,0.000210453,0.0001648168,0.0005151141,0.0001284948],"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":[0.00001300031,0.00003712469,0.0007232119,0.0002120812,0.0003319524,0.00001913521,0.0004716356,0.9868142,0.01091615,0.00009617145,0.000153381,0.0002119557],"study_design_scores_gemma":[0.0009485552,0.00004226748,0.004907969,0.0001693751,0.0001246123,0.00004136144,0.000168021,0.9914221,0.001107828,0.00000973202,0.000794101,0.0002640756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4021338,0.002346865,0.5931145,0.00001530864,0.002056869,0.00009164443,0.00006192923,0.0001311026,0.0000479526],"genre_scores_gemma":[0.9506384,0.00002749446,0.04843196,0.000007235834,0.0003983587,0.00001097396,0.0003935191,0.00006549542,0.0000266149],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5485045,"threshold_uncertainty_score":0.9999635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05066183453967479,"score_gpt":0.2629180599618279,"score_spread":0.2122562254221531,"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."}}