Monitoring the Bacterial and Fungal Biota of Eleven Tobacco Grades Stored at Three Different Locations
Bibliographic record
Abstract
Abstract Tobacco as many other plants has its own microbiota. There are very few studies determining the evolution of this microbiota during tobacco storage, which may affect the quality of tobacco. Polymerase chain reaction (PCR) combined with denaturing gradient gel electrophoresis (DGGE) were used to determine changes in the microbiota of tobacco during the aging of eleven different tobacco grades stored at three different locations for twelve months. The microbial fraction of these tobacco grades was extracted, and the bacterial 16S and the fungal 18S ribosomal RNA gene (rDNA) sequences were PCR amplified before being segregated by DGGE. The bacterial complexity of the tobacco grades was represented by DGGE migrating banding profiles that varied between 20 and 30 bands. Some variations in the banding profiles were observed between the tobacco grades, but overall no substantial changes occurred in the bacterial population of the different grades during their storage at different locations. Most of the fungal DGGE profiles were identical and had only one dominating band related to the genus Aspergillus. Bacterial and fungal isolates were also derived from the microbial fractions of the tobacco, and part of their respective 16S and 18S rDNA sequences were determined. Bacterial isolates belonged to Bacillales and gamma Protobacteria. Fungal isolates belonged to the genus Aspergillus. Our results showed that the bacterial and fungal biota of tobacco are relatively stable throughout 12 months storage time.
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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".