Cattle and Wildlife Competition for Forage: Budget Versus Bioeconomic Analyses of Public Range Improvements in British Columbia
Bibliographic record
Abstract
We analyze the gains of public investments in range productivity when cattle compete for forage with wildlife herbivores. Ignoring extra‐market values by applying conventional budget analyses leads to higher cattle stocking rates and lower overall social benefits of public range improvements. This is demonstrated numerically for public forage in British Columbia, where privately profitable cattle stocking rates on newly seeded range exceed those that are socially optimal, perhaps by as much as double depending on the assumptions one makes about marginal preservation benefits. This highlights the importance of applying dynamic optimization, bioeconomic models to analyze investments in public range. Les auteurs analysent quel gain on tire des sommes publiques investies dans la productivité des grands parcours quand les bovins et les herbivores sauvages se livrent concurrence pour les fourrages. Ne pas tenir compte des valeurs extérieures au marché en recourant à des analyses budgétaires ordinaires entraîne une hausse du taux de chargement et une réduction des avantages sociaux globaux issus de la bonification des grands parcours. Il est possible d'en faire la preuve numérique avec les pâturages publics de la Colombie‐Britannique, où les parcours nouvellement ensemencés supportent une population de bovins lucrative pour l'éleveur mais supérieure au taux de chargement optimal socialement, parfois même du double, selon les hypothèses qu‘on formule sur les avantages d'une préservation marginale. l'étude souligne bien qu'il est important de recourir à une optimisation dynamique, soit d'utiliser les modèles bio‐économiques pour analyser les sommes investies dans l'amélioration des pâturages publics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".