Manipulation of density of <i>Pseudotsuga menziesii</i> canopies: preliminary effects on understory vegetation
Bibliographic record
Abstract
Managing second-growth forests to conserve biodiversity has been proposed by both foresters and conservation biologists. Management, however, can have unintended consequences, including reduction in native species diversity and increased invasion by exotic species. Our goal was to determine if inducing heterogeneity in managed forest canopies could promote a diversity of native species without contributing markedly to invasion by exotic species. We examined 1- and 3-year responses of understory plants to variable-density thinning of 55- to 65-year-old stands of Pseudotsuga menziesii (Mirb.) Franco. Our study stands had been managed either with retention of old-growth legacies (large live trees, dead trees, and fallen trees) and no thinning or with removal of legacies and twice-repeated conventional thinning. Variable-density thinning initially resulted in decreased understory cover but increased importance of 20 native species, including 2 species of trees. Two native species, however, decreased in importance, and 11 exotic species increased in importance. Within 3 years, understory cover recovered, species richness increased by >150%, only four exotic species persisted with increased importance, eight native species (including four graminoids) increased in importance, and seven native species decreased in importance. Variable-density thinning shows promise as part of holistic silvicultural systems applied across landscapes throughout stand rotations and as a technique to restore vegetative complexity to closed-canopy second-growth forests.
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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".