Contrasting research approaches to managing mistletoes in commercial forests and wooded pasturesThis minireview is one of a collection of papers based on a presentation from the <i>Stem and Shoot Fungal Pathogens and Parasitic Plants: the Values of Biological Diversity</i> session of the XXII International Union of Forestry Research Organization World Congress meeting held in Brisbane, Queensland, Australia, in 2005.
Bibliographic record
Abstract
Many mistletoe species are pests in agricultural and forest ecosystems throughout the world. Mistletoes are unusual “weeds” as they are generally endemic to areas where they achieve pest status and, therefore, classical biological control and broad-scale herbicidal control are usually impractical. In North American coniferous forests, dwarf mistletoe ( Arceuthobium spp.) infection results in major commercial losses and poses a public liability in recreation settings. Hyperparasitic fungi have potential as biological control agents of dwarf mistletoe, including species which attack shoots, berries, and the endophytic systems of dwarf mistletoe. Development of an inundative biological control strategy will be useful in situations where traditional silvicultural control is impractical or undesirable. In southern Australia, farm eucalypts are often attacked and killed by mistletoes ( Amyema spp.) in grazed landscapes where tree decline and biodiversity loss are major forms of land degradation. Although long-term strategies to achieve a balance between mistletoe and host abundance are promoted, many graziers want short-term options to treat severely infected trees. Recent research has revisited the efficiency and efficacy of silvicultural treatments and selective herbicides in appropriate situations. The results of recent research on these diverse management strategies in North America and Australia are summarized.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".