Saccharomyces cerevisiae Fermentation of 28 Barley and 12 Oat Cultivars
Bibliographic record
Abstract
As barley and oat production have recently increased in Canada, it has become prudent to investigate these cereal crops as potential feedstocks for alcoholic fermentation. Ethanol and other coproduct yields can vary substantially among fermented feedstocks, which currently consist primarily of wheat and corn. In this study, the liquified mash of milled grains from 28 barley (hulled and hull-less) and 12 oat cultivars were fermented with Saccharomyces cerevisiae to determine concentrations of fermentation products (ethanol, isopropanol, acetic acid, lactic acid, succinic acid, α-glycerylphosphorylcholine (α-GPC), and glycerol). On average, the fermentation of barley produced significantly higher amounts of ethanol, isopropanol, acetic acid, succinic acid, α-GPC, and glycerol than that of oats. The best performing barley cultivars were able to produce up to 78.48 g/L (CDC Clear) ethanol and 1.81 g/L α-GPC (CDC Cowboy). Furthermore, the presence of milled hulls did not impact ethanol yield amongst barley cultivars. Due to its superior ethanol yield compared to oats, barley is a suitable feedstock for ethanol production. In addition, the accumulation of α-GPC could add considerable value to the fermentation of these cereal crops.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".