Grain Thin Stillage Protein Utilization: A Review
Bibliographic record
Abstract
Abstract Ethanol production from grains produces wet grain and thin stillage (TS) as major coproducts. The grain fuel ethanol industry is massive, producing 58 billion L per year in the USA alone, and TS production is four to five times this volume. In short, through its coproducts TS and distiller's grains, the ethanol industry is a major supplier of inexpensive protein. However, obtaining this protein can be costly. In spite of its high water content, TS is typically concentrated by evaporation and then sold as distiller's solubles, or combined with wet grain and dried for use as an animal feed ingredient called “distillers’ dried grains with solubles”. The processes used for protein concentration and TS clarification are reviewed, including the addition of clarifying agents, centrifugation, dissolved air and anoxic gas flotation, filtration, size exclusion, and biorefinery processes. Biorefinery processes are being developed that will lower the energy inputs required for evaporation while greatly improving protein concentration. The protein concentrates could potentially be used in higher‐quality animal feed or even in food products.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".