Patterns and trends of heat and wildfire smoke indicators across rural–urban and social vulnerability gradients in Idaho
Bibliographic record
Abstract
Abstract Climate change poses a grave threat to human health with disparate impacts across society. While populations with high social vulnerability generally bear a larger burden of exposure to and impact from environmental hazards; such patterns and trends are less explored at the confluence of social vulnerability and rural–urban gradients. We show that in rural regions in Idaho, low vulnerability populations had both the highest long-term average and the highest increase rate of exposure to heatwaves from 2002–2020, coincident with a higher population density in low—as compared to high—vulnerability rural census tracts. In urban areas, however, high vulnerability populations accounted for the highest long-term average and increase rate of heatwave exposure; they also accounted for highest population density. Contrary to regional warming trends, population-weighted maximum summer land surface temperature (LST-Max) showed a negative trend across Idaho in the past two decades coincident with increasing neighborhood greenness. Our results show that increasing population density in southern Idaho with a Mediterranean climate and hot summers is correlated with increasing greenness—associated with development of barren land and growing trees planted in former developments—and declining LST-Max. Furthermore, we show that while ambient air quality in the past two decades improved in southern Idaho—consistent with national trends—it worsened in northern Idaho. Wildfire smoke concentrations also increased across Idaho, with pronounced trends in northern Idaho. Our findings indicate that while climatic extremes continue to increasingly threaten human lives, nature-based solutions—such as neighborhood greening, where allowed by environmental and social factors—can mitigate some of the adverse impacts of climate change.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".