Can Air Quality Management Drive Sustainable Fuels Management at the Temperate Wildland–Urban Interface?
Bibliographic record
Abstract
Sustainable fire management has eluded all industrial societies. Given the growing number and magnitude of wildfire events, prescribed fire is being increasingly promoted as the key to reducing wildfire risk. However, smoke from prescribed fires can adversely affect public health and breach air quality standards. Here we propose that air quality standards can lead to the development and adoption of sustainable fire management approaches that lower the risk of economically and ecologically damaging wildfires while improving air quality and reducing climate-forcing emissions. For example, green fire breaks at the wildland-urban interface (WUI) can resist the spread of wildfires into urban areas. These could be created through mechanical thinning of trees, and then maintained by targeted prescribed fire to create biodiverse and aesthetically pleasing landscapes. The harvested woody debris could be used for pellets and other forms of bioenergy in residential space heating and electricity generation. Collectively, such an approach would reduce the negative health impacts of smoke pollution from wildfires, prescribed fires, and combustion of wood for domestic heating. We illustrate such possibilities by comparing current and potential fire management approaches in the environmentally similar landscapes of Vancouver Island in British Columbia, Canada and the island state of Tasmania in Australia.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".