Implementing 100% effective gauge-face lubrication and TOR friction management strategies on MRS heavy haul railroad
Bibliographic record
Abstract
For several years, North American Railroads and various research institutions have been involved in implementing best-practice friction management strategies on heavy haul tracks. During 2006, MRS Railroad visited CPR and was impressed with the friction management strategy that had been developed and implemented. In 2007, MRS invited NRC-CSTT to bring this methodology to Brazil and to develop a customized strategy of 100% effective gauge-face lubrication and top-of-rail friction management. This paper discusses the processes, technologies, results and benefits based on this bestpractice applied to MRS during three years (2007-2009). The business case analysis, quantifying the actual net earnings accrued to MRS as a result of the implementation of Friction Management on test sites, and the forecasted earnings that will occur through the expansion of this strategy to the entire MRS system, will be included in the IHHA presentation in June 2009.
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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 | 0.000 |
| 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 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".