Growth recovery in young, plantation white spruce following artificial defoliation and pruning
Bibliographic record
Abstract
Defoliation by the spruce budworm (Choristoneura fumiferana (Clem.)) was simulated by artificially defoliating trees in a plantation of 12-year-old white spruce (Picea glauca (Moench) Voss) over a 2-year period and then allowing the trees to recover for 3 years. Four treatments were applied: control (C); removal of 50% of the current-year foliage (50); removal of all current-year shoots (100P); and removal of all current-year shoots and some older foliage age-classes (100P+). All treatments increased shoot production. Trees in the 100P treatment completely recovered their foliage mass after 1 year, but trees in the 50 treatment were still affected after 3 years of recovery. Trees in the 100P+ treatment showed poor recovery rates in foliage mass. Only the trees in the 50 treatment completely recovered height growth. After 2 years of defoliation, specific volume increment was reduced by 21.3, 58.1, and 75.3% for the 50, 100P, and 100P+ treatments, respectively. After 3 years of recovery, specific volume increment in the 50 treatment recovered completely, while the 100P and 100P+ treatments were reduced by 34.2 and 79.9%, respectively. Because of the release of suppressed buds following both needle loss only and shoot loss, white spruce may be a reforestation candidate for areas having a high probability of budworm outbreaks.
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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".