Assessment of source contributions to seasonal vegetative exposure to ozone in the U.S.
Bibliographic record
Abstract
W126 is a cumulative ozone exposure index based on sigmoidally weighted daytime ozone concentrations used to evaluate the impacts of ozone on vegetation. We quantify W126 in the U.S. in the absence of North American anthropogenic emissions (North American background or “NAB”) using three regional or global chemical transport models for May–July 2010. All models overestimate W126 in the eastern U.S. due to a persistent bias in daytime ozone, while the models are relatively unbiased in California and the Intermountain West. Substantial difference in the magnitude and spatial and temporal variability of the estimates of W126 NAB between models supports the need for a multimodel approach. While the average NAB contribution to daytime ozone in the Intermountain West is 64–78%, the average W126 NAB is only 9–27% of current levels, owing to the weight given to high O 3 concentrations in W126. Based on a three‐model mean, NAB explains ~30% of the daily variability in the W126 daily index in the Intermountain West. Adjoint sensitivity analysis shows that nationwide W126 is influenced most by NO x emissions from anthropogenic (58% of the total sensitivity) and natural (25%) sources followed by nonmethane volatile organic compounds (10%) and CO (7%). Most of the influence of anthropogenic NO x comes from the U.S. (80%), followed by Canada (9%), Mexico (4%), and China (3%). Thus, long‐range transport of pollution has a relatively small impact on W126 in the U.S., and domestic emissions control should be effective for reducing W126 levels.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 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".