Evaluation of the Feed Value for Ruminants of Blends of Corn and Wheat Distillers Dried Grains
Bibliographic record
Abstract
Recently, biofuel processing has produced a large amount of biofuel coproducts. However, to date, there is little information on the metabolic characteristics of proteins and energy in biofuel coproduct-based rations. The objective of this study was to study the metabolic characteristics of proteins and energy in biofuel coproduct-based rations in terms of (1) chemical and nutrient profiles, (2) protein and carbohydrate subfraction associated with various degradation rate, (3) rumen and intestinal degradation and digestion kinetics, and (4) metabolic characteristics of proteins. Two sources of grain corn were mixed with two sources of biofuel coproducts (wheat-based dried distillers grains with solubles, wDDGS) in ratios of 100:0, 75:25, 50:50, and 25:75%. The study revealed that increasing the biofuel coproduct inclusion level increased most of the nutritional components linearly (P < 0.05) except starch, which linearly decreased. With increasing biofuel coproduct inclusion level, the rumen degradation rate and the effective degradability of organic matter were not affected (P > 0.05), but the effective degradability of starch was decreased (P < 0.05). Effective degradation of crude protein and neutral detergent fiber as well as predicted truly absorbed protein supply in the small intestine and degraded protein balance were increased (P < 0.05). In conclusion, the inclusion of the biofuel coproduct up to 25-50% in rations improved potential nitrogen and energy synchronization for microbial growth and improved truly absorbable protein supply to the small intestine, without altering energy value.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".