Identification of fungal pathogens causing fruit tree dieback in British Columbia
Bibliographic record
Abstract
Field surveys were conducted in British Columbia fruit tree growing regions to determine the incidence of dieback and to identify the main fungal species causing cankers. Fruit trees showing cankers and dieback symptoms were recorded from 94% of orchards and 5.5% of trees surveyed. Overall, higher dieback incidence was observed in cherry than apple with 33% of cherry blocks showing between 5% and 26% of trees affected. Morphological studies along with DNA sequencing of the internal transcribed spacer (ITS) including the 5.8S rDNA, and parts of the translation elongation factor 1-α (TEF1), beta-tubulin (TUB2), and actin (ACT1) genes, identified seven fungi for the first time in fruit trees in Canada, including Calosphaeria pulchella, Cytospora parasitica, Cytospora populicola, Cytospora sorbicola, Ilyonectria robusta, Nectria dematiosa, and Phaeoacremonium minimum. In addition, this study reports for the first time Diplodia mutila and Diplodia seriata from cankers in sweet cherry in Canada. The already known fungal pathogens Neofabraea perennans and Neonectria ditissima were also identified. Pathogenicity studies showed N. ditissima and C. sorbicola to cause the largest vascular lesions in apple and cherry, respectively. This study identified the main fungal pathogens causing tree fruit cankers and dieback in British Columbia providing important information for the development of effective control strategies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".