Enumeration and Identification of 4-Ethylphenol Producing Yeasts Recovered from the Wood of Wine Ageing Barriques after Different Sanitation Treatments
Bibliographic record
Abstract
<p>Aims: This work was aimed at the evaluation of several sanitation procedures on the reduction of total microbial flora and of <em>D. bruxellensis</em> recovered from the inner layers of the barrique’s wood.</p> <p>Methods: A group of used oak barrels tainted by 4-ethylphenol and contaminated with <em>D. bruxellensis</em> were differently sanitized and, afterwards, were dismantled to analyse samples of shaves taken from wood surfaces at different depths. Microbial counts were obtained by the Most Probable Number Technique using broths of general purpose medium and of <em>Dekkera</em>/<em>Brettanomyces </em>differential medium (DBDM).</p> <p>Results: The least inefficient treatment included barrique steaming at low pressure. Uncontaminated samples were only detected under this treatment and in the upper level (0-2 mm) of the staves. With this treatment complete destruction of the contaminating flora was not achieved in any level of stave side surfaces and in grooves. The presence of <em>D. bruxellensis </em>was detected in depths up to 6-8 mm in the wood corresponding to the maximum level of wine penetration.</p> <p>Significance: this work demonstrated that even after current sanitation procedures barriques used in wine maturation pose a severe risk to wine stability due to the presence of <em>D. bruxellensis</em>.</p>
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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 | 0.000 | 0.000 |
| 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".