Investigating Emergency Room Visits for Cardiorespiratory Diseases in Alberta and Ontario, Canada in Relation to Wildfires
Bibliographic record
Abstract
Carbon monoxide (CO) is released during biomass burning events, resulting in decreased air quality and leading to the formation of climate forcing pollutants. An increase in wildfires has resulted in a change to the CO seasonal cycle of the North American Pacific Northwest, when comparing 2012-2018 to 2002-2011. This trend was reported using data from the Measurements of Pollution in the Troposphere (MOPITT) instrument on NASA’s Terra satellite. Similarly, an increase in summertime CO values was identified with the Fourier Transform Infrared (FTIR) spectrometer at the University of Toronto Atmospheric Observatory (TAO), over the same time period. Studies have shown correlations between wildfire smoke exposure and healthcare utilization for cardiovascular and respiratory conditions. Monthly counts of Emergency Department admissions for cardiovascular and respiratory diseases for Alberta and Ontario are investigated in relation to wildfire events in Canada and the USA. MOPITT and TAO FTIR CO columns, the Moderate Resolution Imaging Spectroradiometer (MODIS) burned area product, and provincial burned areas from Natural Resources Canada are assessed to estimate wildfire smoke exposure in the study region. This work aims to evaluate if CO can be used as a complementary tracer for health impacts from wildfire smoke exposure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".