The Effects of Fire on Recreation Demand in Montana
Bibliographic record
Abstract
Abstract Wildfire and prescribed fire have the potential to affect user demand and value for recreation, making such information important to the decision-making process for fire managers. However, such information is not always readily available. We conducted surveys on 22 sites within four national forests in western Montana to determine fire effects on recreation demand for hiking and biking, and net economic benefits to visitors. Net value per trip for hikers was $37. There was no statistical difference for consumer surplus between hiking and biking. Although there were differences in existing visitation between hikers and bikers, there were no statistical differences between the two groups as a result of fire effects. We found that hikers' demand decreased slightly in areas recovering from crown fire and increased in areas recovering from prescribed fire. Bikers' response to both types of fire was the opposite of hikers; for example, bikers showed a slight decrease in annual trips as areas recovered from prescribed fire. Individual value per trip was unaffected by both wild and prescribed fire for both activity groups. Although our recreation demand shifts in response to fire were statistically significant, the magnitude of the predicted changes in demand were not substantial from a managerial perspective suggesting that recreation users in Montana are not affected by fire characteristics resulting from prescribed burns or crown fires. Demand, however, decreased by both user groups as area burned increased and the amount of burn viewed from trails increased, suggesting that the size and extent of burns do affect visitation. West. J. Appl. For. 19(1):47–53.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".