Bibliographic record
Abstract
The past three decades have seen a global wine glut. So far, well-intended but wasteful and expensive market-intervention has failed to drag the wine industry out of a chronic annual oversupply of roughly 15%. Can yeast research succeed where these approaches have failed by providing a means of improving wine quality, thereby making wine more appealing to consumers? To molecular biologists Saccharomyces cerevisiae is as intriguing as it is tractable. A simple unicellular eukaryote, it is an ideal model organism, enabling scientists to shed new light on some of the biggest scientific challenges such as the biology of cancer and aging. It is amenable to almost any modification that modern biology can throw at a cell, making it an ideal host for genetic manipulation, whether by the application of traditional or modern genetic techniques. To the winemaker, this yeast is integral to crafting wonderful, complex wines from simple, sugar-rich grape juice. Thus any improvements that we can make to wine, yeast fermentation performance or the sensory properties it imparts to wine will benefit winemakers and consumers. With this in mind, the application of frontier technologies, particularly the burgeoning fields of systems and synthetic biology, have much to offer in their pursuit of "novel" yeast strains to produce high quality wine. This paper discusses the nexus between yeast research and winemaking. It also addresses how winemakers and scientists face up to the challenges of consumer perceptions and opinions regarding the intervention of science and technology; the greater this intervention, the stronger the criticism that wine is no longer "natural." How can wine researchers respond to the growing number of wine commentators and consumers who feel that scientific endeavors favor wine quantity over quality and "technical sophistication, fermentation reliability and product consistency" over "artisanal variation"? This paper seeks to present yeast research in a new light and a new context, and it raises important questions about the direction of yeast research, its contribution to science and the future of 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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.046 | 0.024 |
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".