Assessing protein availability of different bioethanol coproducts in dairy cattle
Bibliographic record
Abstract
Bioethanol production has led to the production of considerable quantities of different coproducts. Variation in nutrient profiles as well as nutrient availability among these coproducts may lead to an imbalance in the formulation of diets. The objectives of this study were to fractionate protein and carbohydrates by an in situ approach, to determine ruminal availability of nutrients for microbial protein synthesis and to determine protein availability to dairy cattle for three types of dried distiller's grains with solubles (DDGS; 100% wheat DDGS (WDDGS); DDGS blend1 (BDDGS1, corn to wheat ratio 30 : 70); DDGS blend2 (BDDGS2, corn to wheat ratio 50 : 50)) and for different batches within DDGS type using the 2010 DVE/OEB protein evaluation system. The results indicated that all DDGS types are quantitatively good sources of true protein digested and absorbed in the small intestine (DVE values; 177, 184 and 170 g/kg dry matter (DM) for WDDGS, BDDGS1 and BDDGS2, respectively). Rumen degraded protein balances (OEB) values were 159, 82, 65 g/kg DM in WDDGS, BDDGS1 and BDDGS2, respectively. Despite the differences in ruminal availability of nutrients among the different batches of DDGS, the DVE values only differed between the batches of BDDGS1 (194 v. 176 g/kg DM). In conclusion, when DDGS is included in the rations of dairy cattle, variation in its protein value due to factors such as DDGS batch should be taken into consideration.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".