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Record W1782954364 · doi:10.4271/2000-01-1856

Influence of Fuel Aromatics Type on the Particulate Matter and NO<sub>x</sub> Emissions of a Heavy-Duty Diesel Engine

2000· article· en· W1782954364 on OpenAlexafffund
W. Stuart Neill, Wallace L. Chippior, Ömer L. Gülder, Jean Cooley, E. Keith Richardson, Ken Mitchell, Craig Fairbridge

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2000
Typearticle
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsShell (Canada)Imperial Oil (Canada)Syncrude (Canada)National Research Council Canada
FundersGovernment of CanadaShell CanadaSuncor Energy Incorporated
KeywordsParticulatesDiesel fuelHeavy dutyDiesel exhaustEnvironmental scienceDiesel particulate filterDiesel engineWaste managementAutomotive engineeringChemistryEngineering

Abstract

fetched live from OpenAlex

<div class="htmlview paragraph">The influence of fuel aromatics type on the particulate matter (PM) and NO<sub>x</sub> exhaust emissions of a heavy-duty, single-cylinder, DI diesel engine was investigated. Eight fuels were blended from conventional and oil sands crude oil sources to form five fuel pairs with similar densities but with different poly-aromatic (1.6 to 14.6%) or total aromatic (14.3 to 39.0%) levels. The engine was tuned to meet the U.S. EPA 1994 emission standards. An eight-mode, steady-state simulation of the U.S. EPA heavy-duty transient test procedure was followed.</div> <div class="htmlview paragraph">The experimental results show that there were no statistically significant differences in the PM and NO<sub>x</sub> emissions of the five fuel pairs after removing the fuel sulphur content effect on PM emissions. However, there was a definite trend towards higher NO<sub>x</sub> emissions as the fuel density, poly-aromatic and total aromatic levels of the test fuels increased.</div>

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.215
Teacher spread0.208 · 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.

Study designBench or experimental
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

Citations21
Published2000
Admission routes2
Has abstractyes

Explore more

Same venueSAE technical papers on CD-ROM/SAE technical paper seriesSame topicVehicle emissions and performanceFrench-language works237,207