Bibliographic record
Abstract
This article will discuss how Canadian Pacific Railway (CPR) has harnessed the latest train control and planning technologies to drive forward its successful long-train program. The first half of 2009 was a tough time for CPR. In the grip of a sharp global economic downturn, the company saw volumes drop by 35% of capacity. With locomotives and cars lying idle, and uncertainty about the recovery, CPR seized the opportunity to find innovative ways of improving productivity. The article shows how CPR always wanted to run longer trains suing distributed power (DP) but when the railway is busy, testing can cause collateral damage to normal traffic and this was an ideal opportunity to validate the modeling that CPR had already done. The drive to extend train lengths has traditionally been constrained by issues such as excessive in-train slack action, deterioration of brake signal propagation, and stress on infrastructure and equipment. Overcoming these difficulties with the aid of DP offers great rewards. In addition to the obvious benefits of increased payload, DP reduces lateral forces and friction, lowers the impact of heavy trains on track, provides better adhesion and fuel economy, and can assist operations in low temperatures. Furthermore, CPR estimates that labor costs on typical transcontinental trains are 30% lower than they would be if the railroad had stuck with shorter trains.
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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".