Economic impacts of forest pests: a case study of spruce budworm outbreaks and control in New Brunswick, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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) | 0.000 | 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 it