The Two Faces of <i>Lactobacillus kunkeei</i>: Wine Spoilage Agent and Bee Probiotic
Bibliographic record
Abstract
Summary Importance: Lactobacillus kunkeei, also known as the “ferocious lactobacilli”, causes fermentation arrest during wine production1. L. kunkeei co-evolved with honeybees and is an important probiotic for bee and hive health2. In the bee ecosystem, L. kunkeei is one of a suite of lactic acid bacteria that protect the bee and hive from pathogens as well as aid in preservation of sugar-rich hive resources. The protection of sugar-rich resources probably uses similar mechanisms to those inhibiting yeast during grape juice fermentation. Key Observations: The probiotic role of L. kunkeei Unique aspects of L. kunkeei metabolism enabling rapid growth in grape juice Proposed mechanisms of inhibition of yeast fermentation by L. kunkeei The environmental incentive for [GAR+] prion induction by yeast in the presence of L kunkeei and fermentation arrest Impact and Significance: The essential role of L. kunkeei in prevention of bee colony collapse disorder limits the options available to winemakers for control of this pervasive spoilage agent. L. kunkeei can be controlled by sulfur dioxide (SO2) addition3. However, although this organism is sensitive to SO2, our data suggest that other microbes present in juice at the same time may reduce the effective concentration of SO2, thereby enabling growth of L. kunkeei. Clues from the mechanism of arrest of fermentation may help explain the role of L. kunkeei in bee health.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".