Defoliation level interacts with tree species and soil richness to determine volume increment reduction and recovery from simulated spruce budworm attack
Bibliographic record
Abstract
Defoliation and tree species determine growth during spruce budworm outbreaks, with site showing inconsistent effects. We determined effects of artificial defoliation treatments (0%, 50%, 100%, 100%+bud) for 3 years on volume increment of 240 balsam fir, black spruce, and white spruce trees, initially 7–10 years old and 2.3–4.1 m tall, on four soil drainage/richness classes. Current annual increment was significantly affected by species × site and site × defoliation interactions, with DBH as covariate. Specific volume increment (SVI) was significantly affected by a species × site × defoliation treatment interaction. Combining species and sites, volume reductions after 3 years were 21%–28%, 43%–51%, and 55%–66% for 50% defoliation, 100% defoliation, and 100%+bud. SVI was negatively, linearly related to cumulative defoliation. Marginal r2 and conditional r2 showed that 64% of variance of SVI was explained by site, species, cumulative defoliation, and DBH, but 90% was explained by fixed and random variables. After 3 years of defoliation, SVI of white spruce was 21%–23% greater than balsam fir and black spruce, and SVI on rich was 55%–79% greater than on poor sites. Cumulative defoliation was a good predictor of growth, and soil richness had a stronger effect on growth after defoliation than in previous studies.
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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) | 0.001 | 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".