Continental-scale insights into the sugarbeet diffusion juice microbiomes
Bibliographic record
Abstract
ABSTRACT Bacterial contamination of raw diffusion juice poses unique challenges during the sugar extraction process. This study profiled bacterial communities by using full-length 16S rRNA amplicon sequencing and quantified the carbohydrate concentrations in raw diffusion juice samples received from sugar factory regions across the USA and Canada. Juice samples were collected at four time points during the 2021 and 2022 processing campaigns. Firmicutes was the dominant phylum from the raw diffusion juice samples collected during both campaigns and comprised 85.5% of total bacterial abundance. Lactic acid bacteria such as Leuconostoc and Lactobacillus were among the core genera which also dominated the bacterial community in raw diffusion juice. Positive correlations in the abundance of functionally and taxonomically related bacterial communities were identified. During the 2021 campaign, 44 bacterial genera were differentially abundant in raw diffusion juice extracted from sugarbeet roots in Periods 1 to 4. This number declined sixfold during the 2022 campaign to three genera. The concentration of raffinose in raw diffusion juice positively correlated to the relative abundance of Leuconostoc . Furthermore, an in vitro assay was performed to assess the growth dynamics of Leuconostoc mesenteroides in sucrose or raffinose-rich medium and observed the rapid consumption of both carbohydrates by this bacterium. This finding is important for deciphering microbial growth dynamics in raw diffusion juice that can be useful in minimizing sugar loss during the factory processing. IMPORTANCE Findings additionally provide baseline information that can be used to develop mitigation strategies that reduce losses due to microbial contamination of sucrose processing streams.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".