Characterization of microbial communities in ethanol biorefineries
Bibliographic record
Abstract
Bacterial contamination of corn-based ethanol biorefineries can reduce their efficiency and hence increase their carbon footprint. To enhance our understanding of these bacterial contaminants, we temporally sampled four biorefineries in the Midwestern USA that suffered from chronic contamination and characterized their microbiomes using both 16S rRNA sequencing and shotgun metagenomics. These microbiotas were determined to be relatively simple, with 13 operational taxonomic units (OTUs) accounting for 90% of the bacterial population. They were dominated by Firmicutes (89%), with Lactobacillus comprising 80% of the OTUs from this phylum. Shotgun metagenomics confirmed our 16S rRNA data and allowed us to characterize bacterial succession at the species level, with the results of this analysis being that Lb. helveticus was the dominant contaminant in this fermentation. Taken together, these results provide insights into the microbiome of ethanol biorefineries and identifies a species likely to be commonly responsible for chronic contamination of these facilities.
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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 | 0.001 | 0.001 |
| 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".