Optimized harvest planning under alternative foliage-protection scenarios to reduce volume losses to spruce budworm
Bibliographic record
Abstract
Spruce budworm ( Choristoneura fumiferana Clem.) severely defoliates balsam fir ( Abies balsamea (L.) Mill.) and spruce ( Picea spp.) in large periodic outbreaks and represents one of Canada’s most damaging and widespread forest insects. We present a modeling framework that integrates stand-level spruce budworm volume impacts used in the Spruce Budworm Decision Support System (SBWDSS) into an industrial-scale timber supply model for the 209 000 ha Black Brook District in northwestern New Brunswick, Canada. This approach uses linear optimization of harvest scheduling, salvage, and insecticide application to minimize volume reduction. One hundred and ninety-five scenarios were simulated, including normal and severe spruce budworm outbreaks, beginning in 2002, with combinations of varying insecticide efficacy, timing, and spatial extent of protection. After simulated severe defoliation from 2007 to 2016, maximum harvest reductions of 35% were predicted for a normal outbreak for the 2012–2016 period, and 46% for a severe outbreak for the 2017–2021 period; these impacts were reduced to 25% and 34% using re-optimized harvest scheduling and salvage. Results suggest that combined optimized salvage and harvest rescheduling could reduce future harvest reductions by up to 12%. Spatial optimization of protected areas gave similar results to those obtained using protection priority assignments based on marginal stand-level volume reduction in the SBWDSS.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".