A stochastic programming standard response model for wildfire initial attack planning
Bibliographic record
Abstract
Wildfires are responsible for several civilians deaths and millions of dollars in property losses every year, on average. Wildfire containment is the result of an effectively performed initial attack. We formulate a two-stage stochastic integer programming standard response model for initial attack. The model assumes a known standard response needed to contain a fire of given size. The goal of the model is to contain as many fires as possible while minimizing the fixed rental and travel costs and the expected future operational costs. We report on a study based on district TX12, which is one of the fire planning units of the Texas Forest Service (TFS) in East Texas, involving seven operations bases and up to 28 dozers. We use our methodology to position the dozers based on 30- and 60-min maximum travel time restrictions and evaluate each deployment in terms of the number of fire-fighting resources positioned at each operations base and the expected number of escaped (contained) fires. We also consider deploying additional resources to operations bases that need them the most. The deployments made by our methodology provide several insights and show that the original distribution of resources in TX12 at the time of this study was not optimal. For example, more dozers were initially located at operations bases in areas of low density of fires while fewer dozers were located at bases in areas of high density of fires.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".