The threat from ozone to vegetation in Ireland
Bibliographic record
Abstract
Ozone is the most damaging air pollutant to vegetation globally. Metrics of accumulated ozone above a concentration threshold (e.g. AOT40, ppb·h) have been widely used to assess ozone risk. However, there is growing consensus that accumulated Phytotoxic Ozone Dose (POD) above a receptor-specific critical stomatal flux threshold (y; nmol O 3 m −2 s −1 ), expressed per unit of projected leaf area , provides a more reliable risk assessment, as it considers ozone entering the leaf (PODy, mmol m −2 leaf area). Few studies have assessed both concentration- and flux-based metrics using site-specific observations of ozone and meteorology. In this study we assessed the risk that ozone poses to five vegetation types across eight sites in Ireland during 2005–2021, using AOT40 and PODy risk metrics, and we predicted impacts using dose–response relationships. Long-term trends in both metrics were also assessed. The PODy critical level for vegetation protection was exceeded for all vegetation types, with exceedances most common at Atlantic coastal sites, and for tree species (beech POD 1 15.7–25.7 mmol/m 2 PLA). When PODy and AOT40 results were normalised based on their respective critical levels, predicted impacts were higher for PODy. There were significant increases in PODy for three vegetation types at rural sites during the study period, which also experienced increases in temperature and global solar radiation. The long-term trends were consistent with other European studies that show decreases in AOT40 and increases in PODy. While ozone concentrations in Ireland are relatively low (39–75 μg/m 3 five-year average range), the humid climate and longer growing season may lead to elevated stomatal ozone uptake and thereby a risk to vegetation.
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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 | 0.001 |
| 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".