Beyond Bread and Beer: Value-Added Products from Wheat
Bibliographic record
Abstract
Although wheat (Triticum aestivum) and related cereals [Barley (Hordeum vulgare), Rye (Secale cereale) are primarily used for producing baked goods and beverages, cereal crops can be used to create many value-added goods beyond these traditional products. Fractionation of cereal grains and extraction of valuable phytochemicals allows greater access to materials for use in food additives and nutritional supplements. Fermentation for beverage and fuel bioethanol production results in not only renewable fuel, but also a range of other coproducts, including nootropics. In addition to traditional grain fermentation, straw fermentation is also discussed, which further utilizes the whole plant. The main by-product of cereal grain fermentation, wheat stillage, can undergo a range of processes to enhance its value as a animal feeds, as well as extraction of useful compounds. These methods provide a glimpse of the many sequential and divergent processes that may bring us closer to realizing the full potential of wheat and related cereal grains.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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".