Temporal Trends in Ambient Fine Particulate Air Pollution in Grenada, West Indies, 2020
Bibliographic record
Abstract
Routine collection of ambient air quality data is rare in the Caribbean.To assess the potential for health impacts from exposure to ambient fine particulate matter (PM2.5), an exposure study in Grenada was conducted.This study looked to characterize temporal trends of PM2.5 and changes in the concentrations of this pollutant during dust storms.Four fixed-site stationary monitors were installed in Grenada and one on neighbouring island of Carriacou.They continuously captured PM2.5 concentrations between January 6 and October 31, 2020.Regression analyses were performed to describe associations between ambient PM2.5 and meteorological variables.Daily mean PM2.5 concentrations were approximately 2.5 times higher on Saharan dust days than non-Saharan dust days (8.9 vs 3.6 g/m 3 ; p<0.05).Concentrations measured during the June 2020 Saharan dust storm exceeded the World Health Organization's 24-hour guideline.While concentrations of PM2.5 are low in Grenada relative to other countries, they still pose a health hazard.First and foremost, I'd like to thank my supervisor, Dr. Paul Villeneuve.Your guidance aided immensely in the improvement of my writing, conceptual understanding of the topics we worked through, and especially in navigating new territory as the project changed due to the unexpected occurrence of COVID-19.You consistently pushed me to improve and were always there when I needed help, and for that I am thankful.I'd like to thank my committee members, Dr. Daniel Rainham and Dr. DavidMiller.You both saw the things that I could not and consistently provided feedback to improve the project the past two years.I extend great
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".