Bibliographic record
Abstract
Abstract Fermentation is used to create foods and beverages that are enjoyed by people around the world. Similarly, fermentation creates direct opportunities for feed application such as fermented liquid feed or fermented feedstuffs. Other opportunities exist: fermentation followed by extraction of a main product for human or biofuel application also creates co-products that require application in petfood or animal feeds for valorization. Indeed, cereal grains are fermented to produce beer, distilled spirits, or bioethanol and their associated co-products can be fed either wet or dry. For example, traditional beer production using fermentation of barley grain produces abundant brewer’s spent grains and also brewer’s spent hops and yeast as co-products. Brewer’s spent grains are mostly fed wet to ruminants due to its greater fiber content than barley grain and avoiding the cost of its drying required for compound feed application. Wet brewer’s yeast can be used as feedstuff in liquid feed systems for swine. Dried brewer’s yeast can be considered for pet food application due to included nutrients, nucleotides, mannan oligosaccharides, and β-glucans. Other cereal grains such as corn and rice are also used for beer production. Whiskey is produced using fermentation of an array of cereal grains, and distiller’s co-products have traditionally been fed wet or dry mostly to cattle. For the last two decades, large-scale production of ethanol as biofuel has created the co-product distillers dried grains with solubles (DDGS) as commodity feedstuff. Subsequently, DDGS has been used in livestock feed and petfood as protein source. With animal feed application, dietary inclusion of fermentation co-products provides opportunities for circular agriculture whereby nutrients excreted by livestock will be applied to soil to support grain production. Finally, depending on price and quality, fermentation co-products may be part of pet food and livestock feed formulations to achieve competitive cost and functionality.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".