Tackling Tehachapi: Experiments and analyses from the California mountains to the Quebec iron ore fields are quantifying the benefits of friction management
Bibliographic record
Abstract
This article describes results of recent analysis in California on the application of wayside top-of-rail friction control along Union Pacific in the famous Tehachapi Loop. The analysis was done in order to determine the best way to reduce track wear while maintaining enough adhesion for trains on a road with 2% grades and several 10-degree turns. Rail life on the curves is generally three to five years or even less. This was the first time multiple wayside units were installed on this type of track, so that the applicator deployment and output rates would be optimized to permit accurate evaluation of their effects. The revenue-service demonstrations were conducted over a short period of time. Key parameters included system reliability, effectiveness under a range of traffic, and interaction with gauge face lubricant and curving factors and rail wear. Early analysis suggests significant reductions in curving forces and low rail-wear rates. Trains operating up-grade received significantly greater reductions than those with sustained air braking on the downhill. Low rail wear was cut by an average of 58%. High rail results were mixed. Another test looked at a new application technology mounted on a locomotive, which may be particularly suited for captive freight fleets. It is mounted on a freight car directly behind the trailing locomotive, which does not impose the same burdens on locomotive maintenance schedule that loco-mounted applicators do. Field testing shows a modified ore car outfitted with a friction modifier applicator was successful in controlling lateral forces for a 160-car train. The article provides details about track conditions, test sites and economic analysis.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".