15 Years of Air Quality (AQ) Objective Analysis Mapping over North America Using Real-Time Observations and Canadian Operational AQ Forecast Models
Bibliographic record
Abstract
The Regional Deterministic Air Quality Analysis (RDAQA) is a mapping of surface air pollutant concentration which combines numerical forecasts from the Regional Air Quality Deterministic Prediction System (RAQDPS) and hourly AQ observational data from monitoring surface networks over North America. These include Canadian measurement networks operated by the provinces, territories, and some municipalities and those networks covering the continental United States under the umbrella of EPA’s national AIRNow program. The model forecasts and observations are combined based on an optimal interpolation algorithm. The current RDAQA has a horizontal spatial resolution of 10 km and is issued every hour. It provides our best knowledge of the current state of the atmosphere for surface pollutant concentration for chemical species like: ozone, NO2, NO, SO2, PM2.5 , PM10. The RDAQA also provide a mapping of the Air Quality Health Index (AQHI) in quasi-real time and can be used by meteorologists in Environment Canada’s regional forecast offices to inform the public on a daily basis about the health risk associated to short term exposure to air pollution. The RDAQA products available over an extended period of time can also be used by Health Canada and health consortium partners to study how Air Quality and other multiple environmental factors are linked to a wide range of health outcomes. A description of the RDAQA will be presented as well as data access and future plans.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.002 | 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".