Comparisons of Meso-Scale Air Pollution Dispersion Modelling of S02, N02 and 03 Using Regional-Scale Monitoring Results
Bibliographic record
Abstract
Results of a regional-scale monitoring campaign were compared with two meso-scale to sub-continental modelling studies, for S02 and N02 and 03 respectively (Fourie, 2006, Zunckel et al., 2006, van Tienhoven et al., 2006, Van Tienhoven and Zunckel, 2004). However, a direct validation of the monitored results with modelled results could not be carried out, as available modelling studies dealt with different periods from the monitoring study. For this study, three monitoring sites were selected for comparison with modelling results. These sites were strategically selected to be representative of the entire region. Site Elandsfontein in the centre of the industrial Highveld, site Amersfoort, downwind from the central pollution source region and site Louis Trichardt, a remote site. Sulphur, nitrogen and ozone species comparisons were considered in turn. The comparisons were carried out for equivalent annual (and seasonal) cycles. The compa risons produced mixed results. For sulphur and nitrogen species in most cases, depending on site and season, modelling results ranged between significant underestimates to overestimates. Ozone modelling almost always overestimated the concentrations compared to the measured results. Despite several limiting factors, constraining the reliability of the comparisons between the modelled and measured results, they were important as the distribution of the gases showed patterns that imply understanding of the source and fate of these pollutants. The uncertainty in the magnitude of the model inaccuracies as well as margin of error of the measured data remained. Thus a modelling validation is recommended using the concurrent period with fewer uncertainties.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".