Cost effectiveness of fire management strategies in southern Australia
Bibliographic record
Abstract
Fire-management agencies invest significant resources to reduce the impacts of future fires. There has been increasing public scrutiny over how agencies allocate fire-management budgets and, in response, agencies are looking to use quantitative risk-based approaches to make decisions about expenditure in a more transparent manner. Advances in fire-simulation software and computing capacity of fire-agency staff have meant that fire simulators have been increasingly used for quantitative fire-risk analysis. Here we analyse the cost trade-offs of future fire management in the Australian Capital Territory (ACT) and surrounding areas by combining fire simulation with Bayesian Decision Networks. We compare potential future-management approaches considering prescribed burning, suppression and fire exclusion. These data combined costs of treatment and impacts on assets to undertake a quantitative risk analysis. The proposed approach for fuel treatment in ACT and New South Wales (NSW) provided the greatest reduction in risk and the most cost-effective approach to managing fuels in this landscape. Past management decisions have reduced risk in the landscape and the legacy of these treatments will last for at least 3 years. However, an absence of burning will result in an increased risk from fire in this landscape.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".