Microbial diversity in sugarcane ethanol production in a Brazilian distillery using a culture-independent method
Bibliographic record
Abstract
Sugarcane ethanol production occurs in non-sterile conditions, and microbial contamination can decrease productivity. In this study, we assessed the microbial diversity of contaminants of ethanol production in an industrial facility in Brazil. Samples obtained at different stages were analyzed by pyrosequencing-based profiling of bacterial and archaeal 16S rRNA genes and the fungal internal transcribed spacer region. A total of 355 bacterial groups, 22 archaeal groups, and 203 fungal groups were identified, and community changes were related to temperature changes at certain stages. After fermentation, Lactobacillus and unclassified Lactobacillaceae accounted for nearly 100 % of the bacterial sequences. Predominant Fungi groups were "unclassified Fungi," Meyerozyma, and Candida. The predominant Archaea group was unclassified Thaumarchaeota. This is the first work to assess the diversity of Bacteria, and Archaea and Fungi associated with the industrial process of sugarcane-ethanol production using culture-independent techniques.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".