Economics of Early Intervention to Suppress a Potential Spruce Budworm Outbreak on Crown Land in New Brunswick, Canada
Bibliographic record
Abstract
We investigated the potential economic impacts of future spruce budworm (Choristoneura fumiferana Clem.) (SBW) outbreaks on 2.8 million ha of Crown land in New Brunswick, Canada and compared an early intervention strategy (EIS) with foliage protection approaches. We coupled the Spruce Budworm Decision Support System (SBW DSS) with a Computable General Equilibrium (CGE) model to assess the impacts of EIS and foliage protection on 0%, 5%, 10%, and 20% of susceptible Crown (publicly owned) forest, under moderate and severe SBW outbreak scenarios. Cumulative available harvest supply from 2017 to 2067 was projected to be reduced by 29 to 43 million m3, depending upon SBW outbreak severity, and a successful EIS approach would prevent this loss. These harvest reductions were projected to reduce total economic output by $25 billion (CAD) to $35 billion. Scenarios using biological insecticide foliage protection over 20% of susceptible Crown forest area were projected to reduce losses to 6–17 million m3 and $0.5–4.1 billion. Depending upon SBW outbreak severity, EIS was projected to have benefit/cost ratios of 3.8 to 6.4 and net present values of $186 million to $353 million, both higher than foliage protection strategies. Sensitivity analysis scenarios of ‘what if’ EIS partially works (80% or 90%) showed that these produced superior timber harvest savings than the best foliage protection scenario under severe SBW outbreak conditions and generally superior results under moderate outbreak scenarios. Overall, results support the continued use of EIS as the preferred strategy on economic grounds to protect against SBW outbreaks on Crown land in New Brunswick.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".