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Record W2091672456 · doi:10.1115/icef2002-505

Influence of Duty Cycles and Fleet Profile on Emissions From Locomotives in Canada

2002· article· en· W2091672456 on OpenAlexaboutno aff
P. P. Eggleton, Robert O. Dunn

Bibliographic record

VenueDesign, Application, Performance and Emissions of Modern Internal Combustion Engine Systems and Components · 2002
Typearticle
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsnot available
Fundersnot available
KeywordsEngineeringNitrogen oxidesFuel efficiencyAeronauticsTransport engineeringDiesel fuelEnvironmental scienceAutomotive engineeringWaste management

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.200
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2002
Admission routes1
Has abstractyes

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