Bibliographic record
Abstract
Light-duty chassis dynamometer driving cycle tests were conducted on a Mercedes A170 diesel vehicle with various sulfur-level fuels and exhaust emission control systems. Triplicate runs of a modified light-duty federal test procedure (FTP), US06 cycle, and SCO3 cycle were conducted with each exhaust configuration and fuel. The fuels used in these experiments met the specifications of the fuels from the DECSE (Diesel Emission Control Sulfur Effects) program (1, 2, 3 and 4)1. Ultra-low sulfur (3 ppm) diesel fuel was doped to 30 and 150 ppm sulfur so that all fuel properties except sulfur content would be the same. Although the Mercedes A170 vehicle is not certified for sale in the United States, its particulate matter (PM) and nitrogen oxide (NOx) emissions in the as-tested condition were within the Environmental Protection Agency's Tier 1 full useful life standards with its OEM oxidation catalysts installed. Engine-out tests showed that the OEM catalysts reduce PM by 30-40%. There was very little effect of fuel sulfur on the performance of the OEM oxidation catalysts. With a replacement lightoff catalyst and NOx adsorber (lean-NOx trap) installed, FTP NOx emissions were reduced by more than 90% with 3 ppm fuel. Reductions of NOx on the US06 and SC03 cycles were 89% and 96%, respectively. Following the evaluations with the ultra-low sulfur fuel, the catalyst system was poisoned with the equivalent of 3,000 miles on 30 ppm sulfur fuel, which reduced the system's effectiveness to 80% on the FTP, 66% on the US06, and 84% on the SC03.
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 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".