Optimized Utilization of the Co-products from Bioethanol Processing and Oat Grain: Effect of Blending on Biochemical, Biodegradation, and Nutritional Profiles
Bibliographic record
Abstract
The objective of this study was to (1) optimize the utilization of wheat-based dried distillers grains with soluble (wDDGS) by blending with oat ( Avena sativa L.) grain as an alternative feed for beef cattle when the barley price is high and (2) investigate the effect of blending on biochemical, biodegradation, and nutritional profiles. Oat grains were blended with wDDGS produced in western Canada at different levels (4:0, 3:1, 2:2, and 1:3 on %DM basis in two batches, denoted O0, O25, O50, and O75, respectively). The study revealed that increasing the wDDGS resulted in increasing most nutrient contents linearly (P < 0.05) except for starch and cell wall materials, which were linearly decreased (from 43.6 to 12.0% and from 34.5 to 29.1% of DM for starch and NDF, respectively). When wDDGS was increased in the blend/mixture, intestinally absorbable protein and degradable balance of protein increased (P < 0.05). Overall, through blending or combining with the cereal grain, the co-products from bioethanol processing could be optimally utilized. The best combination of oat to wDDGS ratio was 75% to 25%.
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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 | 0.000 | 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 | 0.000 |
| 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 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".