Bibliographic record
Abstract
Long-term strategies to improve rail and wheel wear control on Canadian Pacific Railway's 1100km coal route in British Columbia have reduced rail and wheel costs, and helped to increase axleloads (reducing wagon-km costs by 25%) and reduce fuel consumption by 15%. These strategies included: a switch to Japanese 350-390BHN chrome-alloyed hardened steel rail; overcoming a gauge problem by substituting hardwood sleepers with larger baseplates eccentric to the field to resist overturning; by preferential grinding of the field side of the low rail; producing an artificially worn wheel profile with the addition of 1.6mm of metal in the flange foot to improve steering and reduce creepage and wear; the use of frame brace steerable bogies; and frequent reprofiling of rail to ensure good contact stresses. Inspection procedures for the rails to extend rail wear limits are also described. A programme was undertaken to replace cut spikes as a means of fastening rails to sleepers in curves less than 250m radius with a rolled plate held down by five screw spikes with spring washers and Pandrol e-clips. 370BHN low alloy hypereutechtoid rail steels have been installed recently on curves and trials of new rails with an additional 5.6mm vertical wear have been undertaken. Improvements to rail lubrication are also described.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.089 | 0.039 |
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".