Re-examining wood supply in light of future spruce budworm outbreaks: A case study in New Brunswick
Bibliographic record
Abstract
Forest protection (spraying of biological insecticide), salvage harvesting, and strategic re-planning are typical mitigation options to reduce wood supply impacts caused by spruce budworm (Choristoneura fumiferana [Clem.]) defoliation. However, all such measures are expensive and difficult to implement, and decisions about if and to what extent such measures should be applied hinge on the magnitude of benefits and associated costs. We used an integrated forest estate pest-impact planning model to quantify harvest impacts for three plausible outbreak scenarios, and the effect of these typical mitigation strategies for 3.0 million ha in New Brunswick. Cumulative harvest reductions, relative to the no defoliation case, reached 18% and 25% by 2052 under moderate and severe defoliation patterns, respectively. We demonstrate that up to 30% to 50% of these projected reductions could be avoided through foliage protection treatments, depending on the outbreak scenario. Salvage and re-planning mitigated harvest losses by up to 20% in the short term (20 to 25 years), but had little benefit over the long run (40+ years). Even with aggressive implementation of all mitigation measures, significant harvest impacts (10% reduction from 2017 to 2042) were unavoidable, regardless of outbreak scenario.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".