Estimating forest vulnerability to the next spruce budworm outbreak: will past silvicultural efforts pay dividends?
Bibliographic record
Abstract
Silvicultural treatments recommended to reduce damage by spruce budworm (SBW; Choristoneura fumiferana Clemens) include reducing balsam fir (Abies balsamea (L.) Mill.) abundance and age and increasing spruce (Picea spp.) and hardwood content. To evaluate the effect of these measures on forest timber supply, we assessed stand characteristics, disturbance history, and timber supply for an intensively managed eastern Quebec forest from 1985 to 2004, encompassing a major SBW outbreak. During this time, mean stand age declined from 55 to 51 years, and proportions of areas in balsam fir stands declined (42% to 27%), spruce–fir stabilized (12% to 11%), and mixedwoods increased (32% to 52%). We estimated forest vulnerability using softwood volume reductions following simulated outbreak scenarios of different severity (low, moderate, and high) and different effects of hardwood content in reducing spruce–fir defoliation. Volume reductions for outbreaks simulated to begin in either 1985 or 2004 were similar, ranging from 15%–46% (no hardwood effect in reducing defoliation) to 13%–39% (given a maximum hardwood content effect) for light and severe outbreaks, respectively. Considering the net detrimental effect of increased hardwood content on softwood timber supply, we question the dividends of promoting hardwoods and recommend increasing the combined use of plantations and weeding treatments to increase spruce content.
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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 | 0.003 |
| 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 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".