Deploying Wildland Fire Suppression Resources with a Scenario‐Based Standard Response Model
Bibliographic record
Abstract
Wildland fire managers deploy suppression resources to bases and dispatch them to fires to maximize the percentage of fires that are successfully contained before unacceptable costs and losses occur. Deployment is made with budget constraints and uncertainty about the daily number, location, and intensity of fires, all of which affect initial-attack success. To address the deployment problem, we formulate a scenario-based standard response model with two objective functions: the number of suppression resources deployed and the expected daily number of fires that do not receive a standard response, defined as the desired number of resources that can reach the fire within a specified response time. To determine how deployment levels affect the standard response objective, a weighted sum of the objective functions is minimized, and the weights are ramped from large to small to generate the tradeoffs. We use the model to position up to 22 engines among 15 stations in the Amador-El Dorado unit of the California Department of Forestry and Fire Protection in central California. Each deployment is further evaluated in terms of expected number of escaped fires using CFES2, a stochastic simulation model of initial attack. The solutions of the standard response model form a tradeoff curve where increasing numbers of engines deployed reduces the expected daily number of fires not receiving the standard response. Solutions concentrate engines in a small set of centrally-located stations. We use a simple heuristic with CFES2 to incrementally remove engines based on simulation estimates of expected utilization frequency. The deployments obtained with the heuristic contain about the same number of fires as do solutions of the standard response model, but the heuristic solutions deploy engines to more stations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".