Assessing Population Exposures to Motor Vehicle Exhaust
Bibliographic record
Abstract
The need is growing for a better assessment of population exposures to motor vehicle exhaust in proximity to major roads and highways. This need is driven in part by emerging scientific evidence of adverse health effects from such exposures and policy requirements for a more targeted assessment of localized public health impacts related to road expansions and increasing commercial transportation. The momentum for improved methods in measuring local exposures is also growing in the scientific community, as well as for discerning which constituents of the vehicle exhaust mixture may exert greater public health risks for those who are exposed to a disproportionate share of roadway pollution. To help elucidate the current state-of-the-science in exposure assessments along major roadways and to help inform decision makers of research needs and trends, we provide an overview of the emerging policy requirements, along with a conceptual framework for assessing exposure to motor-vehicle exhaust that can help inform policy decisions. The framework includes the pathway from the emission of a single vehicle, traffic emissions from multiple vehicles, atmospheric transformation of emissions and interaction with topographic and meteorologic features, and contact with humans resulting in exposure that can result in adverse health impacts. We describe the individual elements within the conceptual framework for exposure assessment and discuss the strengths and weaknesses of various approaches that have been used to assess public exposures to motor vehicle exhaust.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".