Exploring Microbial Dysbiosis in Orchards Affected by Little Cherry Disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it