An Assessment of Meteorological Effects on Air Quality in Windsor, Ontario, Canada ― Sensitivity to Temporal Modeling Resolution
Bibliographic record
Abstract
The HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) model was used to study air quality in the City of Windsor (42.16° N, 82.58° W), Ontario, Canada. Two-day back trajectory simulations were conducted for the year of 2003 to investigate the regional transport of air pollutants. Trajectories were then characterized by air mass path direction and regions traversed as dominant factors in regional transport of air pollutants, to assess meteorological effects on air quality in Windsor and provide initial identification of potential upwind pollution source regions. Statistical analysis was conducted to study whether the trajectory simulation results are sensitive to temporal modeling resolution of one, two, three and six simulations per week. It was found that HYSPLIT backward trajectory modeling can provide good quality and consistent results with a temporal resolution of two or three runs per week, comparable to a resolution of six runs per week. The HYSPLIT backward trajectory modeling and analysis methods presented can identify potential source regions of transboundary pollutants at practical temporal modeling resolutions. This is useful to communities and policy-makers developing public health policy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".