Spruce budworm and management effects on forest and wood product carbon for an intensively managed forest
Bibliographic record
Abstract
An integrated forest management optimization model was developed to calculate potential spruce budworm ( Choristoneura fumiferana Clemens) effects on forest and wood product carbon (C) from 2007 to 2057 and to evaluate potential C sequestration benefits of alternative management strategies (salvage, biological insecticide application). The model was tested using simulated spruce budworm outbreaks on a 210 000 ha intensively managed forest in northwestern New Brunswick, Canada. Under a severe spruce budworm outbreak scenario from 2007 to 2020, harvest volume and forest and wood product C storage in 2027 were projected to be reduced by 1.34 Mm3, 1.48 Mt, and 0.26 Mt, respectively, compared with the levels under no defoliation. Under the same severe outbreak scenario, implementation of salvage and harvest replanning plus a biological insecticide applied aerially to 40% of susceptible forest area, reduced harvest, forest C, and wood product C impacts by 73%, 41%, and 56%, respectively. Extrapolation of these results to all of New Brunswick suggests that a future severe spruce budworm outbreak could effectively increase total provincial annual C emissions (all sources) by up to 40%, on average, over the next 20 years. This modeling approach can be used to identify to what extent insecticide application, as a forest-C-offset project, could result in additional C storage than without forest and pest management.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".