Distribution and Long-Term Trends of Tropospheric Ozone Concentrations in Ireland
Bibliographic record
Abstract
Tropospheric ozone (O3) is highly variable over space and time reflecting local production and destruction as well as addition and loss through regional and long-range transport. In this study, O3 concentrations at 11 stations in Ireland and their long-term trends (7–9 sites) were evaluated; O3 concentrations (2015–2019) varied spatially, with the highest annual mean concentrations along the Atlantic west coast (69–75 µg/m3), and the lowest in urban centres (39–43 µg/m3). Ozone followed a seasonal pattern of spring and winter maximum and summer–autumn minimum. Significant long-term (2005–2019) increases were observed in annual O3 concentration at two rural stations, while increases were larger and more frequent during winter with increases at four out of seven stations. During the decade 2010–2019, significant annual increases were observed at four out of nine stations. Observed site- and season-specific increasing trends in O3 concentrations likely reflected changes in regional precursor gas emissions sources. Despite reported decreases in background concentrations in the marine boundary layer in northern mid-latitudes in recent decades, O3 concentrations at some sites in Ireland have increased significantly primarily driven by changes in winter concentrations. There were no significant decreasing trends at any site or in any season.
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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 | 0.001 |
| 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".