On-road Heavy-duty Vehicle Emissions Monitoring System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 it