Experiments for alternative management of forest reserves: effects of partial cutting on stem growth and mortality of large oaks
Bibliographic record
Abstract
Although protected forests are usually kept as free of human disturbance as possible, careful cutting may favour biodiversity in such areas. However, the use of heavy machinery during cutting operations may have a negative impact on protected forests. The large oaks ( Quercus spp.) found in many temperate nature reserves have a rich associated biodiversity. In 25 conservation forests in Sweden, the basal area growth and mortality of 250 large oaks (31–110 cm DBH) were compared between plots that were partially cut to promote the growth and survival of oaks and undisturbed plots. Each forest had one treatment plot (1 ha) and one undisturbed reference plot (1 ha) with similar levels of canopy cover. In 2002–2003, about 25% of the basal area was harvested in treatment plots, thereby increasing canopy openness from 14% to 33%. Cutting increased the mean relative basal area growth of large oaks (49% by plot, 22% by tree) after four seasons (P = 0.026). However, in nine forests, the growth rate was not higher in the treated plot than in the reference plot. Mortality, apparently due to cutting, was recorded only in one forest, where 18 large oaks died. Tree condition (percentage of dead large branches in crown) was the best (negative) predictor of growth rate, and openness around oak crown was also a (positive) predictor. Partial cutting favoured the growth of oaks and may be recommended, but a “hands-off” policy is also effective for these conservation forests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".