The Spruce Budworm Decision Support System: forest protection planning to sustain long-term wood supply
Bibliographic record
Abstract
The Spruce Budworm Decision Support System (SBW DSS) quantifies the marginal timber supply (m 3 /ha) benefits of protecting stands against spruce budworm (Choristoneura fumiferana (Clem.)) defoliation. It allows the user to quantify the volume benefit of protecting alternative areas and determine effects on forest development and annual allowable cut. Implementing the SBW DSS on a land base involves seven steps: (i) defining the base defoliation, or an explicit forecast of the defoliation level included in yield forecasts; (ii) compiling historical defoliation; (iii) defining base volume yields; (iv) obtaining the harvest schedule from the land base management plan; (v) building the stand impact matrix, which quantifies direct impacts of defoliation; (vi) building the forest impact matrix, which quantifies indirect impacts of defoliation on harvest schedules; and (vii) building the stand-history file, which contains all stand-level and defoliation data. These tasks are usually completed every 5 years. The remaining aspects of the planning methodology are implemented annually, including (i) recording the previous year's defoliation, (ii) estimating potential defoliation in the current year from budworm survey data, (iii) calculating volume loss or protection priority, (iv) generating budworm-caused volume loss maps, (v) digitizing potential spray blocks, and (vi) evaluating the protection program. Using ARC/INFO ® and ArcView ® geographic information system programs, the Protection Planning System component (PROPS) generates volume loss maps that can be used to help design and analyze costs and benefits of insecticide spray programs. Implementation of PROPS for the 450 000 ha Upper Miramichi Crown License in New Brunswick is described. Under "normal" and "severe" budworm outbreak scenarios, defined based on predictions of 19992008 defoliation, losses of 6.6 × 10 6 and 16.7 × 10 6 m 3 of spruce (Picea sp.) balsam fir (Abies balsamea (L.) Mill.) volume were projected to occur on this land base.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".