Influence of Duty Cycles and Fleet Profile on Emissions From Locomotives in Canada
Bibliographic record
Abstract
Analyses were carried out of the exhaust emissions from locomotives in Canadian railway operations based on data as of the end of 2001. The authors found that locomotive technology used in the fleet significantly influenced the emission factor while duty cycles had a lesser influence. This is because despite using less fuel for the horse-power produced, the newer diesel engines produce more emissions per unit of fuel consumed. Since 1997, there has been a significant change in the locomotive fleet profile as the Canadian Class I railways replace their 1970s’ era 3,000HP SD-40 type locomotives with modern fuel-efficient 4,300 to 6,000HP locomotives. The new locomotives being introduced, or when re-manufactured, after January 1st 2000 meet the Tier 0 emission standards of the U.S. Environmental Protection Agency. Unlike the U.S.A. where the emissions limits are the subject of legislated standards, the current Canadian situation is a voluntary one aiming to keep, country-wide, locomotive emissions of oxides of nitrogen (NOx) below a cap of 115,000 tonnes per year. The authors’ analyses provide a database upon which trends and scenarios can be examined vis-a-vis the voluntary cap set by the Railway Association of Canada for the period 1995 to 2005 in its Memorandum of Understanding with Environment Canada regarding railway locomotive emissions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".