Comparing the economic value of fire conditions and the effects of wildfire on hiking in New-Mexico recreation sites using contingent the valuation method and travel cost method.
Bibliographic record
Abstract
Abstract: There are some criticizes on Contingent valuation method (CVM) and Travel cost method (TCM), therefore many researchers have combined multiple methods of non-market valuation or have tried to value the non-market goods or services by different methods and check for convergent validity of the methods. In this paper both CVM and TCM are used. The empirical part showed that willingness to pay is more for New-Mexico recreation sites based on a dichotomous choice sample obtained from the data set gathered by Hessaln et al. In addition, fire age is shown to have less impact on WTP of visiting the sites in contingent valuation method. This study showed the two estimates on willingness to pay calculated by TCM and CVM are not significantly different at the 5 % level. Key words:Environmental amenities Non-market valuation Travel Cost method Contingent valuation method Recreation INTRODUCTION process when determining fire management decisions, While fire plays an important role in most forest There are a number of studies that address social ecosystems in North America, the challenge of managing aspects of fire. Englin et al. [2] used travel cost method to fire in North America is to find ways to effectively balance assess value changes for canoeing in Manitoba, Canada. the positive ecological aspects of fire with the negative Hessaln et al. [1] have investigated the fire effects on
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".