Differential response of<i>Pichia guilliermondii</i>spoilage isolates to biological and physico-chemical factors prevailing in Patagonian wine fermentations
Bibliographic record
Abstract
Pichia guilliermondii can produce volatile phenols in the initial stages of wine fermentation; however, its response to different wine stress conditions has been poorly studied. In this work, we present an analysis of the response of 23 P. guilliermondii indigenous isolates to physical and chemical wine stress factors and to indigenous wine killer yeasts. Principal coordinates analysis (PCoA), based on data obtained from response patterns, was carried out to evaluate the relationships among the isolates. Major differences among the isolates were detected in media plates containing 8% ethanol and in those containing 280 g/L glucose. PCoA identified 3 clusters of isolates with different stress response patterns, indicating a relationship between the tolerance to these compounds and the origin of the isolates. Pichia guilliermondii isolates were sensitive to the toxins produced by the species Hanseniaspora uvarum, Metschnikowia pulcherrima, Wickerhamomyces anomala (ex Pichia anomala), and Pichia kluyveri, with a maximum level of sensitivity against W. anomala (91% on average). Those isolates obtained from fermenting must proved to be more resistant to killer yeasts than those obtained from grape surfaces. The combined evaluation of the response to physico-chemical and biological factors presented in this work could be a useful standard protocol for the evaluation of the potential spoilage capacity of yeasts in winemaking.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".