Bioeconomic Modeling of Wetlands and Waterfowl in Western Canada: Accounting for Amenity Values
Bibliographic record
Abstract
This study reexamines and updates an original bioeconomic model of optimal duck harvest and wetland retention by Hammack and Brown (1974, Waterfowl and Wetlands: Toward Bioeconomic Analysis. Washington, DC: Resources for the Future). It then extends the model to include the nonmarket (in situ) value of waterfowl and the ecosystem service and other amenity values of wetlands in addition to the value of ducks to hunters. The focus is the prairie pothole region of Western Canada. Results indicate that wetlands and duck harvests need to be increased relative to historical levels, confirming Hammack and Brown's original conclusions. Including amenity values leads to a significant increase in the quantity of wetlands and hunters’ harvests of ducks relative to models that focus only on hunting values. Dans le présent article, nous avons revu et actualisé le modèle bioéconomique original de Hammack et Brown (1974) sur le prélèvement optimal de canards et la conservation des zones humides. Nous avons ensuite étendu le modèle afin d'inclure la valeur non marchande (in situ) de la sauvagine et des services écosystémiques, la valeur d'agrément des zones humides et la valeur des canards pour les chasseurs. Nous nous sommes concentrés sur la Région des cuvettes des Prairies dans l'Ouest canadien. Nos résultats autorisent à penser qu'il est nécessaire d'augmenter les zones humides et les prélèvements de canards par rapport aux niveaux historiques, ce qui confirme les conclusions formulées dans l'étude de Hammack et Brown. Le fait d'inclure la valeur d'agrément entraîne une augmentation significative du nombre de zones humides et de prélèvements de canards par les chasseurs comparativement aux modèles qui se concentrent uniquement sur la valeur pour la chasse.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".