DNA Barcoding as a Tool for Surveying Cytospora Species Associated with Branch Dieback and Canker Diseases of Woody Plants in Canada
Bibliographic record
Abstract
Background/Objectives: Branch dieback and canker diseases caused by Cytospora species adversely impact the health of woody plants worldwide. Results: During this survey, 59 Cytospora isolates were obtained from symptomatic trees and shrubs growing in southwest Ontario and Saskatchewan, Canada. A DNA barcoding approach combined with morphological characterization identified 15 known species of Cytospora associated with these diseases: C. chrysosperma, C. curvata, C. euonymina, C. hoffmannii, C. kantschavelii, C. leucosperma, C. leucostoma, C. nitschkeana, C. piceae, C. populina, C. pruinopsis, C. pruinosa, C. ribis, C. schulzeri, and C. sorbina. The most common species isolated from multiple hosts were C. sorbina (10), C. chrysosperma (8), C. nitschkeana (6), and C. pruinosa (6). A wide range of host associations, including non-conifer species, was observed for C. piceae. Conclusions: The obtained results contribute to the study of diversity, host affiliation, geographical distribution, and pathogenicity of Cytospora species occurring on woody plants in both natural habitats and agricultural systems. The findings support the effectiveness of using DNA barcodes in fungal taxonomy and plant pathology studies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".