The potential of mixing timber assets to financially offset negative effects of deer browsing on western redcedar
Bibliographic record
Abstract
Western redcedar (WRC) is a highly desirable species in British Columbia's Coastal Western Hemlock zone, both from a management and a conservation perspective. However, it is also highly palatable for ungulates. Existing countermeasures against browsing all have high costs and imperfect results in common. We used the portfolio method to display how diversification can help to lower investment risk. Using risk-return ratios of a WRC and Douglas-fir (DF), we derived species portfolios that yield maximum financial return per unit of risk. Financial indicators were calculated based on Monte Carlo simulations, which consider timber price fluctuation and browsing risk. Results show how economic risks of a forest investment could be reduced by creating a species portfolio. The optimum portfolio leading to most beneficial risk-return combination is 75% WRC and 25% DF if browsing is lowered using protective measures that double planting costs; and 30% WRC and 70% DF if no protective measures are applied. Accordingly, the most desirable risk-return combination is that of a mixed-species forest, whereas the 2 species don't have to be grown in intimate mixtures. Our research sketches a path forward that can help to ensure WRC will remain an important asset in BC's timber portfolio.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".