Nutrient variation and availability of wheat DDGS, corn DDGS and blend DDGS from bioethanol plants
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract BACKGROUND: The dramatic increase in bioethanol production in Canada has resulted in millions of tonnes of different types of new co‐products: wheat dried distillers grains with solubles (DDGS), corn DDGS and blend DDGS (e.g. wheat:corn=70:30). There is an urgent need to systematically evaluate the nutritive value of different types of DDGS. Little research has been conducted to determine the magnitude of the differences in nutritive value among wheat DDGS, corn DDGS and blend DDGS and between different bioethanol plants. The objectives of this study were to compare different types of DDGS and different bioethanol plants in terms of: (1) chemical profiles; (2) mineral concentrations of sulfur (S), calcium (Ca) and phosphorus (P); (3) protein and carbohydrate subfractions associated with different degradation rates; (4) digestible component nutrients and energy values; and (5) in situ rumen availability of each DDGS component. RESULTS: The results showed that chemical profiles differed among wheat DDGS, corn DDGS and blend DDGS. Mineral profiles were different among the three types of DDGS with wheat DDGS lower in S (3.9 vs. 7.2 g kg −1 DM), higher in Ca (1.8 vs. 0.5 g kg −1 DM) and P (9.1 vs. 7.7 g kg −1 DM) than corn DDGS, but similar to blend DDGS. Wheat DDGS had the lowest and corn DDGS had the highest energy values (TDN, DE 3X , ME 3X , NEL 3X for dairy; NE m and NE g beef cattle) while blend DDGS was in between. Wheat DDGS was lower in the intermediately degradable CP fraction (PB2: 277 vs. 542 g kg −1 CP) and higher in the rapidly non‐protein degradable fraction (163 vs. 114 g kg −1 CP) and slowly degradable CP fraction (PB3: 512 vs. 279 g kg −1 CP) than corn DDGS, but similar to blend DDGS. For carbohydrate subfractions, wheat DDGS was higher in non‐structural carbohydrate fraction (NSC: 483 vs. 184 g kg −1 CHO), higher in highly degradable free sugars fraction (CA: 359 vs. 91 g kg −1 CHO), higher in unavailable CHO (CC: 204 vs. 142 g kg −1 CHO), similar in rapidly degradable CHO fraction (average 108 g kg −1 CHO), lower in intermediately degradable CHO (CB2: 313 vs. 674 g kg −1 CHO) than corn DDGS. Wheat DDGS had higher in situ CP degradability and lower NDF degradability than corn DDGS, but similar degradability to blend DDGS. CONCLUSION: Among the three types of DDGS, they differed in chemical characterisation, mineral concentration (S, Ca, P), estimated energy values for both beef and dairy cattle, protein and carbohydrate subfractions, in situ degradability. Bioethanol plants also had significant impact on nutritive value of DDGS. The energy values (DE 3X , ME 3X , NEL 3X , NE m and NE g ) in wheat DDGS were similar to wheat and corn suggesting wheat DDGS as an alternative to wheat and corn in dairy and beef diets. The energy values (DE 3X , ME 3X , NEL 3X , NE m and NE g ) in corn DDGS were significantly higher than in corn, indicating that corn DDGS is superior to corn in dairy and beef diets. The energy values (DE 3X , ME 3X , NEL 3X , NE m and NE g ) in the blend DDGS were higher than that in wheat DDGS but similar to corn DDGS, suggesting blend DDGS as an alternative to corn and superior to wheat and wheat DDGS in dairy and beef diets. Copyright © 2009 Society of Chemical Industry
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it