On-road Heavy-duty Vehicle Emissions Monitoring System
Bibliographic record
Abstract
The introduction of particulate and oxides of nitrogen (NOx) after-treatment controls on heavy-duty vehicles has spurred the need for fleet emissions data to monitor their reliability and effectiveness. The University of Denver has developed a new method for rapidly measuring heavy-duty vehicles for gaseous and particulate fuel specific emissions. The method was recently used to collect 3088 measurements at a Port of Los Angeles location and a weigh station on I-5 in northern California. The weigh station NOx emissions for 2014 models are 73% lower than 2010 models (3.8 vs 13.9 gNOx/kg of fuel) and look to continue to decrease with newer models. The Port site has a heavy-duty fleet that has been entirely equipped with diesel particulate filters since 2010. Total particulate mass and black carbon measurements showed that only 3% of the Port vehicles measured exceed expected emission limits with mean gPM/kg of fuel emissions of 0.031 ± 0.007 and mean gBC/kg of fuel emissions of 0.020 ± 0.003. Mean particulate emissions were higher for the older weigh station fleet but 2011 and newer trucks gPM/kg of fuel emissions were nevertheless more than a factor of 30 lower than the means for pre-DPF (2007 and older) model years.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.015 |
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".