An Analysis of Congestion Effects Across and Within Multiple Recreation Activities
Bibliographic record
Abstract
This paper uses stated and revealed preference data to quantify differential welfare impacts from changes in congestion at outdoor recreation sites that support multiple activities. The results indicate that welfare impacts from congestion vary by congestion point, site, and activity within a given site. Estimating willingness to pay (WTP) for multiple sites and activities allows for parametric testing of whether WTP varies among sites and activities. The most general implication of this study is that if one has the ability to discriminate by activity, doing so is preferable purely in terms of overall model performance. This approach is especially useful for circumstances in which visitor flows in a system of multiple use areas can be managed so as to increase net benefits associated with public lands. Dans la présente étude, nous avons utilisé des données sur les préférences déclarées et révélées pour quantifier l'impact différentiel que des variations de congestion dans les aires d'activités récréatives multiples ont sur le bien‐être. Les résultats ont montré que l'impact différentiel de la congestion sur le bien‐être varie selon le point de congestion, l'aire et l'activité au sein d'une aire donnée. L'estimation de la volonté de payer (VDP) pour des aires et des activités multiples permet de vérifier, à l'aide d'un test paramétrique, si la VDP varie ou non entre les aires et les activités. L'implication la plus générale de cette étude est que si quelqu'un a la possibilité de discriminer selon l'activité, le fait de le faire est préférable purement si l'on considère la performance globale du modèle. Cette méthode est particulièrement utile dans les cas où l'affluence des visiteurs dans un système d'aires polyvalentes peut être gérée de sorte à accroître les avantages nets liés aux terres publiques.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".