Influence of feeding increasing levels of dry or modified wet corn distillers’ grains plus solubles in whole corn grain-based finishing diets on hepatic and renal mass, and glutathione peroxidase and urea cycle enzyme activities in finishing cattle
Bibliographic record
Abstract
Salim, H., Wood, K. M., Cant, J. P. and Swanson, K. C. 2015. Influence of feeding increasing levels of dry or modified wet corn distillers’ grains plus solubles in whole corn grain-based finishing diets on hepatic and renal mass, and glutathione peroxidase and urea cycle enzyme activities in finishing cattle. Can. J. Anim. Sci. 95: 407–415. Forty-two cross-bred steers (BW=357±5.8 kg) fed whole corn grain-based finishing diets were used in a completely randomized block (60, 120, or 180 d on feed) design (2×3 factorial arrangement of treatments plus control) to determine the effect of inclusion level [0 (control), 16.7, 33.3, and 50% of diet DM) and form (dry (DDGS) or modified wet (MWDGS)] of distillers’ grains plus solubles (DGS) on hepatic and renal glutathione peroxidase (GPx) and hepatic urea cycle enzyme activities. Kidney weight (g kg−1 of BW) increased linearly (P=0.004) with increasing inclusion levels of DGS. There were no effects (P≥0.11) of dietary treatment on hepatic and renal GPx activity (U g−1, U mg−1 of protein, and kU liver−1). Hepatic carbamoyl phosphate synthetase activity (kU liver−1 and U kg−1 of BW) tended to linearly increase (P=0.09 and P=0.10, respectively) with increasing inclusion level of DGS. Hepatic ornithine transcarbamoylase and argininosuccinate synthetase activity (kU liver−1 and U kg−1 of BW) increased linearly (P≤0.05) with increasing inclusion levels of DGS. These data indicate that steers adapt to feeding up to 50% DGS by increasing kidney mass and activity of urea cycle enzymes in liver to allow for clearance of excess nitrogen. Also, hepatic and renal GPx activity, as an indicator of Se status, is not affected when typical finishing diets are fed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".