Options for Effective Forest Management of the Oak Wilt Fungus (Bretziella fagacearum) in the Eastern United States
Bibliographic record
Abstract
Oak wilt is a fungal related disease that is caused by the oak wilt fungus (Bretziella fagacearum [Bretz] Z.W. De Beer, S. Marincowitz, T.A. Duong, and M.J. Wingfield) which is an introduced vascular wilt fungus that mainly infects oak species (Quercus spp.). The purpose of this review paper is to provide an update on the environmental challenges that are contributing to the spread of oak wilt as well as providing an overview of the forest management options that are available to reduce its spread. This disease has killed millions of oaks in the Midwest and Texas and impacts forest health in the Eastern United States as well. As climate change shifts ranges northward, the distribution of oak wilt is predicted to spread north to Canada. Forest managers, landscapers, and researchers use various management techniques to mitigate the impacts the disease causes to economy, biodiversity, and aesthetics. Current management emphasizes removal of trees near or in the infection center to limit the overland mode of spread. To contain the underground mode of spread, root graft connections between oak trees needs to be severed. Monitoring is key to limiting the spread of oak wilt and will require a combination of remote sensing based technologies and nventory plot network for effective ground-truthing.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".