Why mountain pine beetle exacerbates a principal–agent relationship: exploring strategic policy responses to beetle attack in a mixed species forest
Bibliographic record
Abstract
The management of public forestland is often carried out by private forest companies, in which case the landowner needs to exercise care in dealing with catastrophic natural disturbance. We use the mountain pine beetle ( Dendroctonus ponderosae Hopkins, 1902) damage in British Columbia to explore how the public resource owner can protect future timber supply while salvaging damaged stands. We examine the variability and timing of beetle attack in a mixed species forest using mathematical programming to schedule harvest but employ the novel strategy of maximizing the timber portfolio at the end of the 20 year time horizon rather than net present value. Various financial and even-flow constraints insure a modicum of stability during the salvage period. We also model supply of adequate feedstock for electricity generation. Based on our study, the optimal short-run response to beetle damage is to increase harvests in stands with 70% or more lodgepole pine ( Pinus contorta var. latifolia Engelm. ex S. Watson) that would otherwise be uneconomic to harvest, similar to operational practice reported by the BC government. The government could focus on stable supply of individual forest products over the time horizon, thereby also stabilizing short-term revenues. Alternatively, it could emphasize an even-flow of total harvest to greatly enhance revenues (which also exhibit greater volatility) and rely more heavily on future harvests of damaged timber. Regardless of the strategy chosen, optimizing future timber supply potential means that a large proportion (about 25% in this study) of damaged pine is left for future harvest, although it will not be of sufficient quality to produce lumber.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".