Fungi from Galleries of the Emerald Ash Borer Produce Cankers in Ash Trees
Bibliographic record
Abstract
The emerald ash borer (EAB, Agrilus planipennis) is a devastating invasive pest that has killed millions of ash trees in the United States and Canada. EAB was discovered in the US in 2002 and first reported in Minnesota in 2009. It attacks ash trees that are native to the United States, including Fraxinus americana (white ash), F. nigra (black ash) and F. pennsylvanica (green ash). It also attacks Chionanthus virginicus (white fringe tree). Seven species of fungi isolated and identified only from EAB-infested trees in a previous study as having the potential to cause cankers were used to test their pathogenicity in F. americana (white ash). The fungi used were Cytospora pruinosa, Diplodia mutila, Diplodia seriata, Paraconiothyrium brasiliense, Phaeoacremonium minimum, Phaeoacremonium scolyti, and Thyronectria aurigera. Two field experiments that used F. americana used two inoculation methods: woodchip and agar plug inoculations. Results indicated that all of the fungi tested caused cankers in varying amounts, as compared to the controls. The largest cankers were caused by D. mutila (270 mm2), C. pruinosa (169 mm2), and D. seriata (69 mm2). All fungi except for T. aurigera were re-isolated and sequenced to confirm Kochs’ postulates. Canker-causing fungi found in association with EAB galleries have the potential to contribute to tree dieback and mortality.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".