Towards a Valid Model of Train Braking System at Low Adhesion Condition
Bibliographic record
Abstract
The Low Adhesion Braking Dynamic Optimisation for Rolling Stock (LABRADOR) project was a good step towards developing a valid train brake system model to be used to assess the train performance in various adhesion conditions.The LABRADOR model has previously been validated in dry conditions.However, for LABRADOR to become a trusted industry tool then it must be seen to provide accurate predictions of the behaviours of real, contemporary trains that are braking in genuinely low adhesion conditions.Test data from trains braking in low adhesion conditions is rare, but the Rail Safety and Standards Board (RSSB) "T1107 Sander Trial" project has carried out an extensive series of tests in order to measure the brake performance benefits of different sander configurations.Based on the diversity of the data and the number of measured variables in each individual test, the data forms a useful resource for LABRADOR improvement and validation.This paper presents LABRADOR validation process under low adhesion conditions.The sander trial data has been used to develop sanding and cleaning (conditioning) effect models that have been integrated within LABRADOR model.The upgraded LABRADOR model then has been tested and simulated under various low adhesion scenarios that represent the experimental tests.The results shows that the model outputs match the experimental data with a good degree of accuracy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".