Bibliographic record
Abstract
In this paper option pricing theory is used to analyse whether or not to preserve a wilderness area. A numerical approach is demonstrated that can be applied to any generalized stochastic process. The impact of assuming that amenity value follows a logistic process, rather than geometric Brownian motion, is considered. The calculation of critical levels for amenity value necessary to justify preserving a wilderness area such as the Killarney Provincial Park in Ontario or the Headwaters Forest in California is demonstrated. The impact of changing the assumed growth and volatility of amenity value is also examined. JEL Classifcation: D81 Q26 Ce mémoire utilise la théorie de la tarification d'une option pour analyser si on doit préserver un espace naturel. On utilise une approche numérique qui peut être appliquée à tout processus stochastique généralisé. On examine l'impact de possibilités que le profil de la valeur de la ressource dans la temps suive un processus logistique plutôt qu'un pattern géométrique de type Brownien. Le texte montre le calcul des niveaux critiques de la valeur de la ressource qui seraient nécessaires pour justifier la préservation d'une espace naturel comme le parc provincial Killarney en Ontario ou les forêts Headwaters en Californie. L'impact de postulats différents quant à la croissance et à la volatilité de la valeur de la ressource est examiné.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".