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Yeast responses to stresses associated with industrial brewery handling: Figure 1

2007· review· en· W2072607401 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFEMS Microbiology Reviews · 2007
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicFungal and yeast genetics research
Canadian institutionsLallemand (Canada)
Fundersnot available
KeywordsBrewingYeastFermentationBiologyIndustrial fermentationFood scienceHigh GravitySaccharomyces cerevisiaeBiotechnologyBiochemical engineeringBiochemistryEngineering

Abstract

fetched live from OpenAlex

During brewery handling, production strains of yeast must respond to fluctuations in dissolved oxygen concentration, pH, osmolarity, ethanol concentration, nutrient supply and temperature. Fermentation performance of brewing yeast strains is dependent on their ability to adapt to these changes, particularly during batch brewery fermentation which involves the recycling (repitching) of a single yeast culture (slurry) over a number of fermentations (generations). Modern practices, such as the use of high-gravity worts and preparation of dried yeast for use as an inoculum, have increased the magnitude of the stresses to which the cell is subjected. The ability of yeast to respond effectively to these conditions is essential not only for beer production but also for maintaining the fermentation fitness of yeast for use in subsequent fermentations. During brewery handling, cells inhabit a complex environment and our understanding of stress responses under such conditions is limited. The advent of techniques capable of determining genomic and proteomic changes within the cell is likely vastly to improve our knowledge of yeast stress responses during industrial brewery handling.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.909
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.148
GPT teacher head0.378
Teacher spread0.229 · 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