Evaluating the Cytometric Detection and Enumeration of the Wine Bacterium, <i>Oenococcus oeni</i>
Bibliographic record
Abstract
Flow cytometry is a high-throughput tool for determining microbial abundance in a range of medical, environmental, and food-related samples. For wine, determining the abundance of Saccharomyces cerevisiae is well-defined and reliable. However, for the most common wine bacterium, Oenococcus oeni, using flow cytometry to determine cell concentration poses some challenges. O. oeni most often occurs in doublets or chains of varying lengths that can be greater than seven cells. This wine bacterium is also small, at 0.2-0.6 μm and may exhibit a range of morphologies including binary fission and aggregated complexes. This work demonstrates a straightforward approach to determining the suitability of flow cytometry for the chain-forming bacteria, O. oeni, and considerations when using flow cytometry for the enumeration of small microorganisms (<0.5 μm). © 2020 International Society for Advancement of Cytometry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".