Exploring COVID-19 Morbidity and Mortality During the First Three Epidemic Waves in Ontario, Canada: A One Health Perspective to Assessing Risk
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it