Identification of<i>Eutypa</i>spp. Causing Eutypa Dieback of Grapevine in Eastern North America
Bibliographic record
Abstract
Eutypa dieback of grapevine is caused by Eutypa lata in production areas with Mediterranean climates in California, Australasia, Europe, and South Africa. Eutypa dieback has also been described in the colder, eastern North American vineyards where cultivars adapted from native Vitis spp. (e.g., Vitis × labruscana 'Concord') are primarily grown. However, the causal agents associated with the diseases in this region have not been conclusively identified. Examination of 48 vineyards showing symptoms of dieback in the northeastern United States (Connecticut, Massachusetts, Michigan, New York, Ohio, and Rhode Island) and Ontario, Canada revealed that vineyards were mainly infected by Eutypa spp. other than E. lata. Multigene phylogenies (internal transcribed spacer ribosomal DNA, β-tubulin, and RNA polymerase II) of isolates recovered from these vineyards indicated that Eutypa dieback is caused primarily by an undescribed Eutypa sp. and E. laevata. Eutypa sp. was recovered from 56% of the vineyards examined, whereas E. laevata and E. lata were less far common (17 and 6%, respectively). Fruiting body morphology and spore dimensions supported phylogenetic separation of the three taxa. Pathogenicity tests conducted on Vitis vinifera 'Chardonnay' in the greenhouse and in the field verified that all three species were able to cause wood canker and to infect pruning wounds, respectively.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".