Comparing growth and mortality of a spruce budworm (<i>Choristoneura fumiferana</i>) inspired harvest versus a spruce budworm outbreak
Bibliographic record
Abstract
Many current forest management regimes stress emulation of natural disturbance events, e.g., spruce budworm (SBW; Choristoneura fumiferana (Clemens)) outbreaks in a balsam fir (Abies balsamea (L.) Mill.) dominated forest, as the preferred method for ensuring sustainability of forest ecosystems. This study compared a SBW-inspired harvest treatment in 25 plots in northern New Brunswick with an uncontrolled SBW outbreak in 30 plots in the Cape Breton Highlands, Nova Scotia. Stand-level measurements before, during, and after each disturbance indicated similar reduction of living stand volume (70% reduction in emulation harvest versus 83% in SBW outbreak), mortality patterns, and lengths of disturbance (4 years of >10% mortality by density of predisturbance stand). Differences for the harvest treatment included higher cumulative postdisturbance blowdown (43% versus 8% of postdisturbance stand density), conversion to hardwood-dominated stands immediately after the disturbance, and faster growth response (immediate release of all species in the harvest treatment versus decreased balsam fir and white spruce (Picea glauca [Moench] Voss) growth and increased white birch (Betula papyrifera Marshall) growth in the SBW outbreak). There were significant differences in stand dynamics following the two disturbances. Results suggest that instead of emulating SBW disturbances, forest managers should be inspired by the spatial and temporal characteristics of SBW-defoliated stands and use significant key features of them to design harvest plans that satisfy management goals.
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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 | 0.000 |
| 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 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".