Identification and Characterization of <i>Sphaerulina vaccinii</i> sp. nov. as the Cause of Leaf Spot and Stem Canker in Lowbush Blueberry and Its Epidemiology
Bibliographic record
Abstract
Septoria leaf spot and stem canker is an important disease of lowbush blueberry, but the causal pathogen has not been accurately identified. Based on sequence analysis of the internal transcribed spacer, translation elongation factor 1 alpha, RNA polymerase II second largest subunit, 28S nuclear ribosomal DNA gene, and β-tubulin genes, the pathogen aligns closely with the genus Sphaerulina. The phylogenetic analyses based on these loci demonstrate that while the pathogen is closely related to the species Sphaerulina amelanchier, it is sufficiently distinct to warrant a new species designation. No ascomata of the teleomorph were found; however, ascospores recovered from leaves fit, morphologically, with the genus Mycosphaerella. The morphological data also support a new species designation. Based on the host that this pathogen infects, we propose the name as Sphaerulina vaccinii and the disease as Sphaerulina leaf spot and stem canker. Under field conditions, it appears that initial inoculum originates from pycnidia on overwintered leaves and stem lesions (cankers) on fruiting stems. More than 90% of the initial inoculum was released during the flowering period from late May through June. Leaf spots began to appear in early June and disease severity increased in a linear manner over time. Secondary inoculum production from diseased foliage was minimal and not considered important epidemiologically. Defoliation resulting from disease began in early July and increased in a nonlinear manner thereafter. Manual defoliation of blueberry stems at various times prior to harvest showed the substantial extent to which premature defoliation by this disease can affect yield. Stem lesions were also shown to have an impact on yield, even though stems were not killed.
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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 | 0.001 | 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".