Mass tracking for chemical analysis: the causes of ozone formation in southern Ontario during BAQS-Met 2007
Bibliographic record
Abstract
Abstract. A three-level nested regional air pollution model has been used to study the processes leading to high ozone concentrations in the southern Great Lakes region of North America. The highest resolution simulations show that complex interactions between the lake-breeze circulation and the synoptic flow lead to significant enhancements in the photochemical production and transport of ozone at the local scale. Mass tracking of individual model processes show that Lakes Erie and St. Clair frequently act as photochemical ozone production regions, with average mid-day production rates of up to 3 ppbv per hour. Enhanced ozone levels are evident over these two lakes in 23-day-average surface ozone fields. Analysis of other model fields and aircraft measurements suggests that vertical circulation enhances ozone levels at altitudes up to 1500 m over Lake St. Clair, whereas subsidence enhances ozone over Lake Erie in a shallow layer only 250 m deep. Mass tracking of model transport shows that lake-breeze surface convergence zones combined with the synoptic flow can then carry ozone and its precursors hundreds of kilometers from these source areas, in narrow, elongated features. Comparison with surface mesonet ozone observations confirm the presence, magnitude, and timing of these features, which can create local ozone enhancements on the order of 30 ppbv above the regional ozone levels. Sensitivity analyses of model-predicted ozone and HOx concentrations show that most of the region is VOC-limited, and that the secondary oxidation pathways of aromatic hydrocarbons have a key role in setting the region's ozone and HOx levels.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".