Responses of 2 epiphytic yeasts to foliar infection by <i>Rhizoctonia solani</i> or mechanical wounding on the phylloplane of tall fescue
Bibliographic record
Abstract
A growth-chamber experiment was conducted to determine how foliar disease or wounding affects the ability of 2 phylloplane yeasts (Rhodotorula glutinis and Cryptococcus laurentii) to colonize leaves of tall fescue (Festuca arundinacea). Yeasts were applied separately and together onto healthy leaves, leaves infected with Rhizoctonia solani (diseased), and mechanically bruised (wounded) leaves. In all 3 trials, the leaf disturbance treatment significantly affected the abundance of yeast on the phylloplane of tall fescue. Yeast abundance on the diseased or wounded leaves was significantly greater than on the nontreated, healthy leaves. In 2 of the 3 trials, the yeast species applied also had a significant affect on yeast abundance. Typically, R. glutinis was significantly more abundant than C. laurentii when applied individually, but not significantly greater than the total yeast colony-forming units of the co-inoculated treatment. When the 2 yeasts were co-inoculated onto the leaves, R. glutinis comprised 89.7%, 75.4%, and 67.6% of the recovered yeast colony-forming units on healthy, diseased, and wounded leaves, respectfully. Our data suggest that these 2 species of yeasts will differentially colonize compromised leaf tissue with disease or wounds favoring populations of R. glutinis over C. laurentii.
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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 | 0.000 | 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 | 0.001 |
| 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".