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Record W2230921658 · doi:10.4271/2003-01-3144

Emissions from Heavy-Duty Diesel Engine with EGR using Fuels Derived from Oil Sands and Conventional Crude

2003· article· en· W2230921658 on OpenAlexafffund
W. Stuart Neill, Wallace L. Chippior, Jean Cooley, Mike Doma, Craig Fairbridge, Robert Falkiner, Robert L. McCormick, Ken Mitchell

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2003
Typearticle
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsShell (Canada)Imperial Oil (Canada)Suncor Energy (Canada)Syncrude (Canada)National Research Council Canada
FundersGovernment of CanadaShell Canada
KeywordsEnvironmental scienceDiesel engineDiesel fuelAutomotive engineeringWaste managementHeavy dutyPetroleum engineeringExhaust gas recirculationDiesel exhaustEngineeringInternal combustion engine

Abstract

fetched live from OpenAlex

<div class="htmlview paragraph">The exhaust emissions from a single-cylinder version of a heavy-duty diesel engine with exhaust gas recirculation (EGR) were studied using 12 diesel fuels derived from oil sands and conventional sources. The test fuels were blended from 22 refinery streams to produce four fuels (two from each source) at three different total aromatic levels (10, 20, and 30% by mass). The cetane numbers were held constant at 43. Exhaust emissions were measured using the AVL eight-mode steady-state test procedure. PM emissions were accurately modeled by a single regression equation with two predictors, total aromatics and sulphur content. Sulphate emissions were found to be independent of the type of sulphur compound in the fuel. NO<sub>x</sub> emissions were accurately modeled by a single regression equation with total aromatics and density as predictor variables. PM and NO<sub>x</sub> emissions were significantly significantly affected by fuel properties, but crude oil source did not play a role.</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), Insufficient payload (model declined to judge)
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 score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.013
GPT teacher head0.231
Teacher spread0.218 · 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

Citations9
Published2003
Admission routes2
Has abstractyes

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