Influence of Fuel Aromatics Type on the Particulate Matter and NO<sub>x</sub> Emissions of a Heavy-Duty Diesel Engine
Bibliographic record
Abstract
The influence of fuel aromatics type on the particulate matter (PM) and NOx 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. The experimental results show that there were no statistically significant differences in the PM and NOx emissions of the five fuel pairs after removing the fuel sulphur content effect on PM emissions. However, there was a definite trend towards higher NOx emissions as the fuel density, poly-aromatic and total aromatic levels of the test fuels increased.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".