Economic impacts of forest pests: a case study of spruce budworm outbreaks and control in New Brunswick, Canada
Bibliographic record
Abstract
We investigated the potential economic impacts of future spruce budworm (SBW) ( Choristoneura fumiferana (Clemens)) outbreaks on 2.8 million hectares of Crown forest land in New Brunswick by coupling an advanced Spruce Budworm Decision Support System (SBW DSS) model with a dynamic computable general equilibrium model. A total of 16 alternative scenarios were evaluated, including two SBW outbreak severities (moderate versus severe), four SBW control program levels (protecting 0%, 10%, 20%, and 40% of susceptible Crown land forest area), and two pest management strategies (“without” versus “with” replanning harvest scheduling and salvage). The “without” replanning harvest scheduling and salvage strategy findings indicated that, under uncontrolled moderate and severe SBW outbreaks, total output in the New Brunswick economy over the 2012–2041 period would decline in present-value terms by CDN$3.3 billion and $4.7 billion, respectively. SBW control via aerial spraying was shown to reduce the negative impacts on output by up to 66% when protecting 40% of susceptible area. Combining SBW control with replanning harvest scheduling and salvage strategy under moderate and severe outbreaks would reduce the negative impacts on output by a further 1%–18% depending on the level of control implemented. These findings can help forest managers assess the direct and indirect economic effects of forest pest disturbances on regional economies and can also be used together with other sustainable forest management indicators to help broaden the scope of SBW and other forest pest management decision-making.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".