The 4th Brewing Yeast Fermentation Performance Congress, Oxford, England, 9–12 September, 2003
Bibliographic record
Abstract
Hot topics brewing in Oxford…… The taste, texture and aroma of beer are primarily dictated by the performance of yeast during the fermentation process. In turn, many factors affect brewing yeast physiology and can have an adverse impact on beer quality – for example: malt wort composition, ethanol toxicity, cold stress/heat shock, osmostress, hydrostatic and CO2 pressure, reactive oxygen species and cellular ageing. An understanding of such factors may lead to better quality control of brewing yeast cultures and, consequently, more efficient and consistent fermentation in breweries. In essence, good yeast fermentation performance equates to good beer! This was the fourth in a series of biennial congresses held in Oxford on the subject of brewing yeast, and brought together around 50 yeast scientists and brewers from Europe and overseas (including Japan, Canada, Brazil, USA, S. Africa and Australia). The general theme, as in previous congresses, was fermentation performance of brewing yeast. This relates to the following desirable yeast attributes: good (but not extensive) yeast growth, efficient conversion of malt sugars to ethanol, CO2 and secondary fermentation metabolites, maintenance of high yeast viability, genetic stability of industrial yeast strains, and correct yeast flocculation characteristics. Understanding and, more importantly, controlling these aspects are central to the success of many yeast-based industries, not just beer brewing. Three days of the conference covered several topics (in lectures and posters) of direct relevance to brewing yeast practice: genomes/strain identification, culture handling, nutrition, stress, fermentation (progress/monitoring), and petite mutations, presented in six technical sessions: Greg Casey (Coors Brewing Ltd., USA) kick-started the meeting by reviewing methods for brewing-yeast strain differentiation. The assessment of yeast culture purity and genetic stability was viewed by Greg to be crucial in modern large-scale breweries that may be handling several strains at a time (e.g. for contract brewing). Chromosome …
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.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".