Algorithm to Estimate the Lateral Position of Wheel-Rail Contact and Corresponding Rail Profile Radius
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article develops and validates an algorithm to estimate the lateral position of wheel-rail contact and the corresponding rail profile radius. The lateral contact position and contact radius are two novel rail profile performance measures that enable more refined characterization of the rail profile and its influence on rail wear and vehicle dynamics. Leveraging recent advancements in rail profile monitoring techniques, the algorithm contributes to rail maintenance research and practice by developing new measures of performance based solely on commonly available rail profile data. The algorithm developed in this article is an automated process that estimates the lateral position of wheel-rail contact and the corresponding rail profile radius along a rail segment. It uses measured rail profile data as an input and applies rigid contact theory to model contact between a linear wheel profile and the rail profile. The lateral contact position and contact radius are calculated using computer programming that provides graphical and numerical results on a profile-by-profile basis as well as summary statistics for each rail segment. This methodology produces expected results when subjected to validation tests. The validation process analyzes the rationality of algorithmic output against a series of expected results using rail profile data from selected tangent segments of a closed-loop captive-fleet North American rail transit property. The algorithm output does not significantly deviate from any of the expected results, and as such, the algorithm is considered validated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it