Inclusion of sunflower seed and wheat dried distillers’ grains with solubles in a red clover silage-based diet enhances steers performance, meat quality and fatty acid profiles
Bibliographic record
Abstract
The current study compared beef production, quality and fatty acid (FA) profiles of yearling steers fed a control diet containing 70 : 30 red clover silage (RCS) : barley-based concentrate, a diet containing 11% sunflower seed (SS) substituted for barley, and diets containing SS with 15% or 30% wheat dried distillers' grain with solubles (DDGS). Additions of DDGS were balanced by reductions in RCS and SS to maintain crude fat levels in diets. A total of two pens of eight animals were fed per diet for an average period of 208 days. Relative to the control diet, feeding the SS diet increased (P<0.05) average daily gain, final live weight and proportions of total n-6 FA, non-conjugated 18:2 biohydrogenation products (i.e. atypical dienes) with the first double bond at carbon 8 or 9 from the carboxyl end, conjugated linoleic acid isomers with the first double bond from carbon 7 to 10 from the carboxyl end, t-18:1 isomers, and reduced (P<0.05) the proportions of total n-3 FA, conjugated linolenic acids, branched-chain FA, odd-chain FA and 16:0. Feeding DDGS-15 and DDGS-30 diets v. the SS diet further increased (P<0.05) average daily gains, final live weight, carcass weight, hot dressing percentage, fat thickness, rib-eye muscle area, and improved instrumental and sensory panel meat tenderness. However, in general feeding DGGS-15 or DDGS-30 diets did not change FA proportions relative to feeding the SS diet. Overall, adding SS to a RCS-based diet enhanced muscle proportions of 18:2n-6 biohydrogenation products, and further substitutions of DDGS in the diet improved beef production, and quality while maintaining proportions of potentially functional bioactive FA including vaccenic and rumenic acids.
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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) | 0.000 | 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) | 0.001 | 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".