An economic analysis of management practices to mitigate butt rot and deer browse of planted western redcedar
Why this work is in the frame
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Bibliographic record
Abstract
We consider the economic feasibility of silviculture investments to reduce butt rot (through stump removal) and ungulate browse damage (stand establishment strategies), which are the most serious impacts to planted western redcedar (Thuja plicata Donn ex D. Don) stands in coastal British Columbia, Canada. We find mixed support for these investments, even if carbon sequestration benefits are included. We do find butt rot causes significant material damage to volumes, but such damage tends to occur well into the future of the stand diminishing the negative impact on stand value. As such, given the high costs of stump removal, and despite losses of high-quality logs, we find little support for stump removal except under very low discount rates (2%). Deer browse impacts are found to occur in the early stages of stand development, and projected stands should sufficiently recover volumes and value by harvest age. However, under positive carbon prices, because deer browse mitigation measures have an immediate impact on biomass accumulation in the early stages of stand development, we find some conditions for which low-cost deer browse mitigation options might be economically supported on forestlands. Finally, we found that increased planting of seedlings is likely a low-cost, financially attractive option under a broad set of conditions, even on sites without risk to damage, meaning a possible no-regrets strategy to mitigate damages from either deer browse or decay. The benefits of planting highlight the feasibility of using tree breeding to increase growth, resistance to deer, decay, and drought. The methods developed in the paper to evaluate the impact of both root rot and ungulate browsing could be applied to other ecosystems elsewhere.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it