Effects of a Severe Mountain Pine Beetle Epidemic in Western Alberta, Canada under Two Forest Management Scenarios
Bibliographic record
Abstract
We used a simulation model to investigate possible effects of a severe mountain pine beetle (Dendroctonus ponderosaeHopkins) epidemic under two management scenarios in Alberta, Canada. Our simulated outbreak was based on the current epidemic in British Columbia, which may kill close to 80% of the province's pine volume. Our two management scenarios were conventional harvest and a pine-reduction strategy modeled on a component of Alberta's Mountain Pine Beetle Management Strategy. The pine strategy seeks to reduce the number of susceptible pine stands by 75% over the next 20 years through targeted harvesting by the forest industry. Our simulations showed that the pine strategy could not be effectively implemented, even if the onset of the beetle outbreak was delayed for 20 years. Even though we increased mill capacity by 20% and directed all harvesting to high volume pine stands during the pine strategy's surge cut, the amount of highly susceptible pine was reduced by only 43%. Additional pine volume remained within mixed stands that were not targeted by the pine strategy. When the outbreak occurred in each scenario, sufficient pine remained on the landscape for the beetle to cause the timber supply to collapse. Alternative management approaches and avenues for future research are discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".