Effects of an early intervention management strategy for spruce budworm on balsam fir growth
Bibliographic record
Abstract
When an outbreak of spruce budworm ( Choristoneura fumiferana ) occurs, larvae primarily defoliate balsam fir ( Abies balsamea ) trees in the boreal forest, leading to large-scale tree mortality and reduced growth that can cause forests to become carbon sources. In response to the current spruce budworm outbreak in eastern North America, managers have been trialling a new “early intervention strategy” (EIS). Under this strategy, areas are sprayed with insecticides ( Bacillus thuringiensis var. kurstaki (Btk)) to maintain larval population below outbreak levels, preventing defoliation and tree mortality. In this study, we investigated how the management of a spruce budworm outbreak is affecting the growth of fir trees on the island of Newfoundland. Specifically, we took cores from trees in sprayed (n = 60 trees) and unsprayed (n = 109 trees) sites and measured annual ring widths from 2010 to 2024. We then standardized the ring widths and performed a segmented regression to determine whether growth changed over time. We found that growth only changed in unsprayed sites with a significant breakpoint in the tree growth shortly after the outbreak started. Insecticide spraying as applied under the EIS appears to delay growth loss in balsam fir in the short-term. However, the benefits of spraying must be weighed against potential costs to assess the value of this active management approach over the long-term.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".