On the effect of upwind emission controls on ozone in Sequoia National Park
Bibliographic record
Abstract
Abstract. Sequoia National Park (SNP) experiences the worst ozone (O3) pollution of any national park in the U.S. SNP is located on the western slope of the Sierra Nevada Mountains, downwind of the San Joaquin Valley (SJV), which is home to numerous cities ranked among the most O3-polluted in the U.S. Here, we investigate the influence of emission controls in the directly upwind SJV city of Visalia on O3 concentrations in SNP over a 12-yr time period (2001–2012). We show that export of nitrogen oxides (NOx) from the SJV plays a larger role in driving high O3 in SNP than does transport of O3. As a result, O3 in SNP has been more responsive to NOx emission reductions as a function of increasing downwind distance from the SJV. We report O3 trends by various concentration metrics, but do so separately for when environmental conditions are conducive to plant O3 uptake and for when high O3 is most common, which are time periods that occur at different times of day and year. We find that precursor emission controls have been less effective at reducing O3 concentrations in SNP in springtime, which is when plant O3 uptake in Sierra Nevada forests has been previously measured to be greatest. We discuss the implications of regulatory focus on high O3 days in SJV cities on O3 concentration trends and impacts in SNP.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".