Is there an inherent conflict in managing fire for people and conservation?
Bibliographic record
Abstract
Wildfires are a natural disturbance in many ecosystems, creating challenges for land management agencies who need to simultaneously reduce risk to people and maintain ecological values. Here we use the PHOENIX RapidFire fire behaviour simulator to compare fuel treatment strategies that meet the twin objectives of reducing wildfire risk to human settlements and a fire sensitive endangered species, the koala (Phascolarctos cinereus) in south-eastern Australia. The local koala population is in decline and a conservation management plan is being prepared to exclude wildfire for a 10-year period to assist with population recovery. Twelve scenarios developed by the land management agencies were compared using four indicators: wildfire size; burn probability; impact from exposure to fire; and treatment cost. Compared with the current risk setting, three treatment scenarios were found to reduce wildfire size and burn probability concurrently to both people and koalas. These strategies worked by increasing the landscape area treated, which came with increased financial cost. However, the impact from exposure to fire for both property and koala habitat remains high. Additional complementary strategies beyond landscape fuel reductions are needed to reduce impact from exposure in the event of a wildfire.
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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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".