Evaluation and Comparison of Portable Emissions Measurement Systems and Federal Reference Methods for Emissions from a Back-Up Generator and a Diesel Truck Operated on a Chassis Dynamometer
Bibliographic record
Abstract
There is considerable interest in portable emissions measurement systems (PEMS) for emission inventory and regulatory applications. For this study, four commercial PEMS were compared with a Federal Reference Method (FRM) for measuring emissions from a back-up generator (BUG) over steady-state loads and a diesel truck on transient and steady-state chassis dynamometer tests. The agreement between the PEMS and the FRM varied depending on the pollutant and the particular PEMS tested for both the BUG and chassis dynamometer testing. The best performing PEMS for both the BUG and chassis testing was within approximately 12% for NOx of the FRM. For the BUG testing, several PEMS showed agreement with the FRM within approximately 5% for CO2. For the chassis dynamometer testing, the best PEMS showed agreement typically within approximately 5% for CO2. PM measurements for the BUG testing were low compared to the FRM, with the best measurements approximately 20% lower. For the chassis testing, two PM PEMS showed a good correlation but a high bias, while the correlation was worse for the other two PEMS. For each emissions component, some PEMS under different test conditions showed considerably larger deviations than those for the best performing PEMS.
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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".