Wind-driven gap development in Birkley Wood, a long-term retention of planted Sitka spruce in upland Britain
Bibliographic record
Abstract
Management of planted forests in Britain is changing to incorporate biodiversity and other nontimber values by lengthening rotations, seeking alternatives to clear-felling, and identifying stands for nonintervention. However, there are particular uncertainties over stand development of exotic species on areas of high windthrow hazard. This study considered the effect of strong winds between 1987 and 2000 on stand development of Sitka spruce ( Picea sitchensis (Bong.) Carrière) planted in 1923. Windthrown gap area increased eightfold through at least 38 separate events. Windthrow of trees assessed as stable occurred during periods of higher wind speed than those with windthrow of trees assessed as vulnerable, and there was a nonlinear relationship between wind speed and increase in gap area. Gap expansion contributed more to the increase in gap area than new gap formation, and the centre of the largest gap migrated downwind. Nevertheless, a substantial proportion of the stand remains undisturbed to an extent not predicted. Therefore, a complex stand structure may develop, including a partial wave of natural regeneration of Sitka spruce in the expanding gaps, but species diversification will be slow due to the absence of local seed sources. The results provide insights for stand dynamics in windy, maritime environments.
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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".