Prescribed Burning Costs and the WUI: Economic Effects in the Pacific Northwest
Bibliographic record
Abstract
Abstract Federal fuels managers are increasingly using prescribed fire to decrease hazardous fuels and risks to resources in wildland and urban settings. Two factors have become apparent throughout the last several years: prescribed burning costs are rising, and costs exhibit substantial variability (NIFC 2003). Federal fire managers are bound by federal policy to allocate resources efficiently, yet this is difficult without a full understanding of the cost structure of fuels management. Previous studies have examined factors influencing costs but have also grappled with a lack of consistent or reliable data. This study uses FASTRACS (Fuel Analysis, Smoke Tracking, Report Access Computer System), a database maintained by the Pacific Northwest region of the Forest Service and Bureau of Land Management. The database provides information for Washington and Oregon on costs, physical site characteristics, and managerial concerns for fuels management activities. Using multiple regression analysis, we show that the cost of fuels management is influenced by the wildland-urban interface, number of acres treated, designated protection areas, slope, elevation, treatment type, fire regime, agency, and season. Prescribed burning in the wildland-urban interface increased costs, ceteris paribus, 139%. Findings with respect to physical site characteristics were similar to those found in previous research.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".