Towards best-practice management of mistletoes in horticulture
Bibliographic record
Abstract
Mistletoe is increasingly being reported as a horticultural pest, infecting many species grown commercially for fruit, nuts, and other food products. Unlike mistletoe impacts on forestry, the published research on mistletoe in horticulture is scant, with management guidelines reliant on anecdotes, un-replicated trials on unrelated species, and often in different countries and growing systems. We have integrated the existing work to summarize information on the most effective control strategies for mistletoe in horticulture, and call attention to the paucity of empirical research. Despite grower interest in growth regulators and herbicides, limited trials suggest chemical treatment of mistletoe is ineffective, consistent with findings from forestry and ornamental trees. Although labour-intensive, ongoing mechanical removal is the most effective strategy to minimize mistletoe impacts but, without information available on effects of mistletoe infection on yield or tree mortality, cost-effectiveness calculations are not possible. Given the range of herbivores that consume mistletoe tissues, biological control may be useful, both to prevent initial infection and also reduce impacts on infected hosts in commercial plantations. To catalyse more research on mistletoes in horticulture, we articulate six priorities for further work, emphasizing the utility of tree crops as model systems to address questions regarding mistletoe ecology and host-parasite dynamics more broadly.
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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".