Differentiation of <i>Corynespora cassiicola</i> and <i>Cercospora</i> sp. in leaf-spot diseases of <i>Hydrangea macrophylla</i> using a PCR-mediated method
Bibliographic record
Abstract
Mmbaga, M. T., Kim M.-S., Mackasmiel, L. and Klopfenstein, N. B. 2015. Differentiation of Corynespora cassiicola and Cercospora sp. in leaf-spot diseases of Hydrangea macrophylla using a PCR-mediated method. Can. J. Plant Sci. 95: 711–717. Corynespora cassiicola and Cercospora sp. have been identified as the most prevalent and destructive leaf-spot pathogens of garden hydrangea [Hydrangea macrophylla (Thunberg) Seringe] in the southeastern USA, but they are often difficult to accurately detect and distinguish because they often occur together in a disease complex with other pathogenic leaf-spot fungi and produce very similar symptoms. This study was conducted to provide diagnostic PCR primers for detecting and distinguishing Corynespora cassiicola and Cercospora sp. among other leaf-spot pathogens of garden hydrangea. Two primer pairs showed specificity to Corynespora cassiicola and one primer pair showed specificity to Cercospora sp., and these primers did not amplify DNA from any other common fungal pathogens associated with hydrangea leaf-spot diseases. Results from this study show that DNA-based diagnostic primers provide a useful tool for pathogen detection/identification in hydrangea leaf-spot disease, which is an essential step toward understanding disease etiology and developing/applying appropriate disease-management practices in the southeastern USA.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".