Health, Coping Strategies, & Adaptation to Drought- Driven Poor Air Quality in Saskatchewan
Bibliographic record
Abstract
As greenhouse gas emissions increase worldwide, the planet is continuing to warm, changing water amounts and timing. Drought frequency in Canada is expected to increase due to glacier retreat, decreased duration of seasonal snow cover, earlier snow melt, and changing precipitation, along with resulting conditions such as dust storms and wildfires. The predicted increase in drought conditions and resulting exposures to poor air quality demonstrates the importance of researching the impacts of drought conditions on human health, coping methods, and adaptation strategies in the Canadian context due to the relatively few existing studies. This study will look at the wider impacts of drought on the health of Saskatchewan populations as well as coping strategies and adaptation methods of Indigenous groups in Saskatchewan in the face of drought conditions. Studying marginalized communities, such as Indigenous communities who face specific exposures due to their ties to the land, is essential because these communities are likely to experience significant structural barriers and limits to their adaptation given drought impacts. It is important to work with Indigenous communities to understand place-based impacts and culturally appropriate adaptation strategies to inform policy and practice. This project aims to answer the following questions using a coupled human and environment approach of assessing meteorological drivers of drought-induced poor air quality on health: The outcomes of this project are to understand the weather conditions that exacerbate air quality as a result of drought to better inform early warning systems and to enhance knowledge, particularly in a Saskatchewan First Nations context, for evidence informed policies, education, and awareness.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".