Relationship between Twist, Jerk, and Speed: Twist-Tolerance Values and Measuring Chords
Bibliographic record
Abstract
Twist is a track defect that affects comfort by inducing jerk and rolling and endangers the derailment safety of a vehicle by wheel off-loading. Currently, there is no formula in the literature that relates speed and jerk with twist. In this paper, a relationship between jerk, speed, and twist is developed. The relationship was used to assess the tolerance values of twist for design, construction, and maintenance of the track. The relation was also used to assess slow speed on a twisted track beyond the maintenance limit. The advantage of the formulas is that they are speed specific. The relation between twist, jerk, and speed was theoretically validated. The derived equations from the aforementioned relation to estimate tolerance values of twist were validated by comparing with the values given by US and European specifications. A literature review and analysis was performed on chord length to measure the twist. A chord equal to the wheel base is suggested to measure twist on a newly constructed or renewed track. On a revenue track, chord lengths equal to both wheel base and truck center to center distance are recommended to capture both short- and long-wave twist defects. The suggested chord lengths would integrate vehicle parameters, track defects, comfort, and safety.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".