Evaluation of<i>Saccharomyces cerevisiae</i>Wine Yeast Competitive Fitness in Enologically Relevant Environments by Barcode Sequencing
Bibliographic record
Abstract
Abstract When a wine yeast is inoculated into grape juice the potential variation in juice composition that confronts it is huge. Assessing the performance characteristics of the many commercially available wine yeasts in the many possible grape juice compositions is a daunting task. To this end we have developed a barcoded Saccharomyces cerevisiae wine yeast collection to facilitate the task of performance assessment that will contribute to a broader understanding of genotype-phenotype relations. Barcode sequencing of mixed populations is used to monitor strain abundance in different grape juices and grape juice-like environments. Choice of DNA extraction method is shown to affect strain-specific barcode count in this highly related set of S. cerevisiae strains; however, the analytical approach is shown to be robust toward strain dependent variation in DNA extraction efficiency. Of the 38 unique compositional variables assessed, resistance to copper and SO2 are found to be dominant discriminatory factors in wine yeast performance. Finally, a comparison of competitive fitness profile with performance in single inoculum fermentations reveal strain dependent correspondence of yeast performance using these two different approaches.
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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 | 0.000 | 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) | 0.001 | 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".