Exploring Microbial Dysbiosis in Orchards Affected by Little Cherry Disease
Bibliographic record
Abstract
The phytoplasma ‘ Candidatus Phytoplasma pruni’, a causative agent of little cherry disease (LCD), has become an increasing problem for sweet cherry growers in Washington State, which is the largest producer of cherry fruit in the United States. The control of LCD currently relies on the identification and removal of infected trees, which has proven to be difficult because of the prolonged asymptomatic but still contagious state of the disease, and the lack of reliable and economical tests. Thus, the development of new approaches for early detection of LCD will be an important step in the successful control of this tree fruit disease. To identify potential microbial indicators of ‘ Ca. P. pruni’ infection, we evaluated the bacterial and fungal communities in the roots of cherry trees from two different orchards that were (i) infected with ‘ Ca. P. pruni’ and symptomatic; (ii) infected with ‘ Ca. P. pruni’ but remained asymptomatic; and (iii) healthy, with non-‘ Ca. P. pruni’-infected trees. We found significant variation in the microbiomes between the two cherry orchards, with the location being a stronger driving factor determining the fungal compared with the bacterial community. The fungal communities were less affected by the disease conditions compared with the bacterial microbiome. Overall, this study demonstrates the feasibility of the microbiome approach for the early detection of LCD caused by ‘ Ca. P. pruni’ but also demonstrates that more orchards need to be sampled because location was a stronger contributor to the microbiome of cherry tree roots than disease condition.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".