Long‐term SO<sub>2</sub>dispersion modeling over a coastal region
Bibliographic record
Abstract
Air dispersion modeling over coastal regions has proven to be a remarkable challenge in the field of air quality. Many conventional plume dispersion models, such as ISC2 and HYSPLIT, are unable to model such dispersion with the precision that is necessary to accurately predict ground-level concentrations in coastal areas. Considering this, the present work was carried out with two primary objectives: i) to evaluate the effectiveness of currently available mathematical models in predicting plume dispersion over a coastal region and ii) to study the impact of sulfur dioxide emissions from a petroleum refinery over a different community located in the adjacent area. This study demonstrates that CALPUFF predictions are more reliable compared to those of the other models studied, however the operation of CALPUFF is highly data intensive and in many instances, it is difficult to obtain all required input data. This is a particular problem for regions outside ofthe United States of America where sufficient data is difficult to obtain. In addition, the study concluded that the predicted annual average SO2 concentrations in the nearby communities are well within regulatory limits.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".