Multiple Regression Analysis of the Impact of Track Geometry on Wheel-Rail Forces
Bibliographic record
Abstract
One of the fundamental keys to improving track safety standards is to establish a strong correlation between track geometry variations and wheel-rail force parameters that are indicators of vehicle-track safety performance. In this study, wheel-rail forces were collected during field tests of a loaded lumber car and an empty tank car. Computer models of the two tested freight cars were built, and the models were calibrated using field test results. The computer models were then used to evaluate the impact of varying track geometry parameters on track safety using the maximum single wheel L/V ratio, maximum truck side L/V ratio, and minimum vertical wheel load ratio. It was confirmed again that the correlations between these force parameters and any individual geometry parameter were weak. With further investigation, it was found that much better correlation can be achieved using multiple regression techniques to define each wheel-rail force parameter as a function of all track geometry parameters combined together. Expressions of the maximum truck side L/V ratio, maximum single wheel L/V ratio, and minimum vertical wheel load ratio were obtained as functions of curvature, cross level, alignment, gauge, and cant deficiency using multiple regression analysis.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".