The role of vegetation management for enhancing productivity of the world's forests
Bibliographic record
Abstract
The management of competing vegetation has evolved with forest management over the past half century and is now an integral part of modern forestry practice in many parts of the world. Vegetation management, primarily using herbicides, has proven especially important in the establishment of high-yield forest plantations. There has been a substantial amount of research quantifying the wood yield gains from the management of competing vegetation over the past few decades. We reviewed results from 60 of the longest-term studies in North America (Canada and US), South Africa, South America (Brazil) and New Zealand/Australia. About three-quarters of the studies reported 30–500 per cent increases in wood volume from the most effective vegetation treatments. In North America, where the longest-term studies for a variety of tree species were between 10 and 35 years old (or from 20–100 per cent of rotation age), gains in wood volume ranged from 4–11 800 per cent in Pacific north-western forests, 14–5840 per cent in the south-eastern forests, and 49–5478 per cent in northern forests. In South Africa and South America (Brazil), several full-rotation (6–8 years) studies with eucalyptus indicate 29–122 per cent and 10–179 per cent increases in wood volume yield, respectively, from effective vegetation management. In New Zealand, time gains of 1 to 4 years from early vegetation control in radiata pine plantations translated into 7–27 per cent increases in wood volume yield over a 25- to 30-year rotation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".