Tropospheric Ozone Assessment Report: Present-day tropospheric ozone distribution and trends relevant to vegetation
Bibliographic record
Abstract
This Tropospheric Ozone Assessment Report (TOAR) on the current state of knowledge of ozone metrics of relevance to vegetation (TOAR-Vegetation) reports on present-day global distribution of ozone at over 3300 vegetated sites and the long-term trends at nearly 1200 sites. TOAR-Vegetation focusses on three metrics over vegetation-relevant time-periods across major world climatic zones: M12, the mean ozone during 08:00–19:59; AOT40, the accumulation of hourly mean ozone values over 40 ppb during daylight hours, and W126 with stronger weighting to higher hourly mean values, accumulated during 08:00–19:59. Although the density of measurement stations is highly variable across regions, in general, the highest ozone values (mean, 2010–14) are in mid-latitudes of the northern hemisphere, including southern USA, the Mediterranean basin, northern India, north, north-west and east China, the Republic of Korea and Japan. The lowest metric values reported are in Australia, New Zealand, southern parts of South America and some northern parts of Europe, Canada and the USA. Regional-scale assessments showed, for example, significantly higher AOT40 and W126 values in East Asia (EAS) than Europe (EUR) in wheat growing areas (p < 0.05), but not in rice growing areas. In NAM, the dominant trend during 1995–2014 was a significant decrease in ozone, whilst in EUR it was no change and in EAS it was a significant increase. TOAR-Vegetation provides recommendations to facilitate a more complete global assessment of ozone impacts on vegetation in the future, including: an increase in monitoring of ozone and collation of field evidence of the damaging effects on vegetation; an investigation of the effects on peri-urban agriculture and in mountain/upland areas; inclusion of additional pollutant, meteorological and inlet height data in the TOAR dataset; where not already in existence, establishing new region-specific thresholds for vegetation damage and an innovative integration of observations and modelling including stomatal uptake of the pollutant.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".