The potential role of background ozone on current and emerging air issues: An overview
Bibliographic record
Abstract
It is now widely recognized that background ozone may form a significant part of the concentration and, thus, of the health and ecosystem impacts, experienced at many locations. This can be extremely important, as the background concentration is the level below which concentrations cannot be reduced by local efforts alone. In discussing background ozone, it is important to be clear what is meant by the term. Slightly different interpretations of the exact understanding of what constitutes background widen the uncertainty in reported values. Nevertheless, the central tendency to reported background ozone concentrations may be estimated as 25 to 40 ppb for measurements made worldwide. The large majority of recently reported work indicates that these background concentrations are increasing at a rate ranging up to about 0.3 to 0.5 ppb per year. Ozone lifted to levels above the boundary layer can be transported very long (intercontinental) distances. As a result, Asia, North America, and Europe can all contribute to each other’s background ozone concentrations. In this context, the rapid industrial growth in Asia and the expected accompanying increased precursor emissions, could prove to be significant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".