Fungal Composition and Diversity of the Tobacco Leaf Phyllosphere During Curing of Leaves
Bibliographic record
Abstract
Rhizopus oryzae causes tobacco pole rot in China during tobacco flue-curing. Flue-curing is a post-harvest process done to prepare tobacco leaves and involves three different stages: the yellowing stage has the lowest temperatures and highest humidity, then the color-fixing stage has higher temperatures and medium humidity, and finally the stem-drying stage has the highest temperatures and lowest humidity. In this study, fungal culturing and IonS5TMXL high-throughput sequencing techniques were used to reveal the fungal community of the petioles and lamina of tobacco leaves infected with pole rot during flue-curing. A total of 108 fungal isolates belonging to 6 genera were isolated on media. The most common fungal species isolated was the pathogen, R. oryzae, that was most often found equally on petioles and laminas in the color-fixing stage, followed by saprotrophs, mostly Aspergillus spp.. High-throughput sequencing revealed saprotrophs with Alternaria being the most abundant genus, followed by Phoma, Cercospora and Aspergillus, whereas Rhizopus was the tenth most abundant genus, which was mostly found on petioles at the yellowing stage. Both culturable fungal diversity and fungal sequence diversity was higher at stem-drying stage than the yellowing and color-fixing stages, and diversity was higher with leaf lamina than petioles revealing that the changes in fungal composition and diversity during the curing process were similar with both methods. This study demonstrates that the curing process affects the leaf microbiome of tobacco during the curing process, and future work could examine if any of these saprotrophic fungi detected during the curing of tobacco leaves may be potential biocontrol agents for with pole rot in curing chambers.
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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) | 0.000 | 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 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".