K9® Auxiliary Power Unit - Locomotive Idle Reduction System A New Technology towards Reducing GHG Emissions from the Railroad Sector
Bibliographic record
Abstract
Locomotives have historically been allowed to idle for extended periods of time when not in operation in order to protect the engine from freezing. Depending on their assignment, locomotives typically idle 8-14 hours a day, sometimes even more. This has resulted in large quantities of harmful emissions into the atmosphere, particularly NOx, SO2and GHG. In this paper we introduce the K9regAuxiliary Power Unit (APU) for locomotives, an idle reduction system that controls locomotive engine shutdown. The APU consists of an Engine Shutdown Timer coupled with a generator set that is connected to the fluid system of the main engine. This configuration allows for the safe shutdown of the locomotive while monitoring and maintaining the fluid temperature and battery voltage, as well as providing auxiliary power for air conditioning/heating for the cab. The APU system results in large fuel savings, as well as emission and noise reduction. The APU is also equipped with a GPS communication device that allows remote monitoring of the locomotive and quantification of emissions reductions and fuel savings. The APU has been recognized as a system that enables the creation of NOx and SO2emission reduction credits by the Ontario Ministry of the Environment and has the potential to be a strong mechanism of GHG emissions mitigation for the railroad sector.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.014 |
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".