Effects of bioethanol plant and coproduct type on the metabolic characteristics of the proteins in dairy cattle
Bibliographic record
Abstract
The dramatic increase in bioethanol production in Canada has resulted in millions of tonnes of different types of coproducts: wheat dried distillers grains with solubles (DDGS), corn DDGS, and blend DDGS (e.g., wheat:corn 70:30). The objectives of this study were 1) to investigate the effect of DDGS type and bioethanol plant on the metabolic characteristics of the proteins and the total truly digested and absorbed protein supply to dairy cattle using the DVE/OEB system and 2) to compare the metabolic characteristics of the proteins of original feedstock grains with their respective derived DDGS samples. The results showed that all types of DDGS are a good source of the truly digested and absorbed protein in the small intestine [DVE; 107 vs. 249 g/kg of dry matter (DM) for wheat and wheat DDGS; 108 vs. 251 g/kg of DM for corn and corn DDGS]. According to the DVE/OEB system, the predicted total DVE supply to dairy cattle differed among wheat DDGS (DVE=249 g/kg of DM), corn DDGS (DVE=251 g/kg of DM), and blend DDGS (DVE=281 g/kg of DM) and, to a lesser extent, between the different bioethanol plants (DVE: 277 vs. 230 g/kg of DM for bioethanol plants 1 and 2). The results indicated the superior protein value of blend DDGS as well as that of the more optimum degraded protein balance (DPB) value for corn DDGS (DPB: 11 g/kg of DM in corn DDGS vs. 72 g/kg of DM in wheat DDGS and 55 g/kg of DM in blend DDGS). In addition, differences in the acid detergent-insoluble crude protein content of wheat DDGS samples were reflected in differing protein DVE values. In conclusion, it is inappropriate to assume fixed protein values for DDGS without considering factors such as DDGS type and bioethanol plant origin.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".