Economic analysis of geospatial technologies for wildfire suppression
Bibliographic record
Abstract
Geospatial technologies used to fight large fires are becoming increasingly available, yet no rigorous study exists of their effects on suppression costs or fire losses, nor do we know whether these technologies allow more efficient combination of firefighting assets used to suppress fires. The high cost of these technologies merits an assessment of these values. Using data from all large-scale fires originating on US Forest Service land greater than 1620 ha in the Northern Rocky Mountains for the 2000–03 fire seasons, we estimate random parameter models of total fire expenditures, agency fire suppression costs, fire duration, and area burned. Site factors, geospatial technology use, and firefighting assets are used as explanatory variables in these regressions. In addition, stochastic cost frontier models are estimated for suppression costs to judge the efficiency of input use for fires with and without geospatial technology use. We find that although geospatial technology use does not appear to significantly increase suppression costs when other factors are controlled, it does seem to allow more efficient allocation of resources such as labour and capital by fire managers seeking to minimise the costs of controlling large fires. Both of these results suggest that the high cost of using these technologies may be offset by improvements in the use of costly firefighting assets by fire managers.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".