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Record W2067924848 · doi:10.4161/bbug.19687

The winemaker’s bug

2012· review· en· W2067924848 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueBioengineered · 2012
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsnot available
FundersAustralian GovernmentNational Cancer Research InstituteGovernment of South AustraliaAlberta Water Research Institute
KeywordsWineWinemakingBiotechnologyQuality (philosophy)YeastYeast in winemakingBiologySaccharomyces cerevisiaeBusinessFood scienceGenetics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.998
Threshold uncertainty score0.705

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.103
GPT teacher head0.287
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it