A review of the potential for the use of bioherbicides to control forest weeds in the UK
Bibliographic record
Abstract
There are a number of weeds, both native and exotic, which are of considerable economic and ecological importance in UK forestry. These weeds compete with young trees for resources, and suppress their growth in commercial plantations as well as native woodlands. Chemical and cultural control methods are expensive and, in many situations, have not prevented the spread of forest weeds. An alternative, or additional method of weed control, is biological control using bioherbicides. Using this approach, native fungi pathogenic on the target weed are developed and applied inundatively, in a similar manner to that of chemical herbicides. The damage caused by fungi to the weed reduces its impact in young plantations and native habitats. Four important forest weed species in the UK – bracken, bramble, Japanese knotweed and rhododendron – are reviewed here in terms of their biology and impact, current control options and their potential for control using bioherbicides. Despite having a serious impact in forestry, bracken, bramble and Japanese knotweed are not deemed suitable target weeds for the development of a bioherbicide. Considerable effort has already been directed into bioherbicide control of bracken in the UK, without success. Bramble has proved difficult to control using bioherbicides in Canada, and its degree of importance as a forest weed in the UK is probably not as great as for other weeds. Japanese knotweed is already under investigation for biological control using the classical approach, for which it is most suited. Rhododendron, however, is considered a suitable target weed for control using the bioherbicide approach. Of greatest potential is the application of a wood‐rotting fungus as a bioherbicide stump treatment for rhododendron – an approach already developed for weedy hardwood species in South Africa, Canada and The Netherlands.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".