The summer of smoke: ecosocial and health impacts of a record wildfire season in the Northwest Territories, Canada
Bibliographic record
Abstract
BackgroundBetween June and August 2014, Canada's Northwest Territories experienced their worst wildfire season on record, with prolonged smoke events and poor air quality. In total, 385 separate fires burned 3 400 000 hectares of land costing CAN$56·1 million in firefighting expenses. In the context of climate change, this study sought to explore the lived experience of the 2014 wildfire season among four communities in the Northwest Territories.MethodsQuantitatively, we explored associations between air quality (PM 2.5 level), emergency room admissions for respiratory and cardiac events, and dispensations of salbutamol (non-prescription inhaler) during the summer of 2014 compared to 2012 and 2013. Qualitatively, we conducted 30 semi-structured interviews in four communities (Yellowknife, N’Dilo, Dettah, and Kakisa). Interviews were video recorded, and the audio portion of each interview was transcribed to facilitate analysis and theme generation.FindingsBetween June 15 and August 31, 2014, 55% of days had a PM 2·5 above 25 μg/m3, compared to 4% of days in 2012 and 9% of days in 2013. The highest daily PM 2.5 in 2014 was 320·4 μg/m3. Emergency room visits for asthma and pneumonia increased in 2014 compared to 2012 and 2013, but the number of cardiac-related visits did not change. Primary care visits for cough, asthma, and pneumonia and dispensations for prescription inhalers (salbutamol) also increased in 2014 relative to 2012 and 2013. Interviewees reported how their experiences of evacuation and isolation and feelings of fear, stress, and uncertainty contributed to acute and long-term negative effects on their mental and emotional wellbeing. Prolonged smoke events were linked to extended time indoors and respiratory problems. Livelihood and land-based activities were disrupted for some Indigenous interviewees, which had negative consequences for mental, emotional, and physical wellbeing. Individual and community stories of adaptation and resilience before and during the summer were shared; however, there was consensus about the need for improved risk communication and coordination at the community and territorial level to address similar events in the future.InterpretationCoordinated community-based education, communication, and adaptation initiatives that are inclusive of local knowledge, values, and context are needed to address the expressed needs of community members associated with prolonged smoke events and wildfire seasons.FundingHealth Canada.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".