Variety features differentiate microbiota in the grape leaves
Bibliographic record
Abstract
The dependence of plant health and crop quality on the epiphytic microbial community has been extensively addressed, but little is known about plant-associated microbial communities under natural conditions. In this study, the bacterial and fungal communities on grape leaves were analyzed by 16S rRNA gene and internal transcribed spacer high-throughput sequencing, respectively. The results showed differences in the composition of the microbial communities on leaf samples of nine wine grape varieties. The most abundant bacterial genus was Pseudomonas, and the top three varieties with Pseudomonas were Zinfandel (22.6%), Syrah (21.6%), and Merlot (13.5%). The most abundant fungal genus was Alternaria, and the cultivar with the lowest abundance of Alternaria was Zinfandel (33.6%), indicating that these communities had different habitat preferences. The linear discriminant analysis effect size of all species showed that the bacteria Enterococcus, Massilia, and Kocuria were significantly enriched on the leaves of Merlot, Syrah, Cabernet Sauvignon, respectively; Pseudomonadales and Pantoea on Zinfandel; and Bacillus, Turicibacter, and Romboutsia on Pinot Noir. Similarly, the fungi Cladosporium, Phoma, and Sporormiella were significantly enriched on Zinfandel, Lon, and Gem, respectively. Both Bray–Curtis and unweighted UniFrac revealed that bacteria and fungi have a significant impact (P < 0.01), and the results further proved that variety is the most important factor affecting the microbial community. The findings indicate that some beneficial or harmful microorganisms existing on the wine grape leaves might affect the health of the grape plants and the wine-making process.
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How this classification was reachedexpand
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 | 0.000 |
| 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 teacher head, 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".