Exploring COVID-19 Morbidity and Mortality During the First Three Epidemic Waves in Ontario, Canada: A One Health Perspective to Assessing Risk
Bibliographic record
Abstract
Livestock farming serves to support human sustenance and livelihood, but these systems also emit atmospheric particulate matter ≤ 2.5 µm (PM2.5) and ammonia (NH3), which are known respiratory stressors. Over three epidemic waves in Ontario, Canada, prolonged exposure to PM2.5 and NH3 were explored as risk factors for COVID-19 incidence and mortality. Through multilevel negative binomial principal component (PC) regression modeling, regional variations in PM2.5 were positively associated with COVID-19; the strength of this association declined as the pandemic continued. Compared to livestock farming, fuel combustion appeared to have had a more prominent role in the observed association of PM2.5 with COVID-19. There was a minor inverse association between NH3 and COVID-19, suggesting that livestock farming communities, as opposed to more urbanized communities, had a tendency toward a decreased risk of COVID-19 health outcomes; this result may reflect confounding. In this thesis, PC regression served as an effective tool for enabling a robust One Health risk factor analysis. PC regression can be recommended for studying intricate relationships in the One Health context.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".