337 Supplementation of a Kaolin and Yeast-Based Feed Additive on Performance of Feedlot Cattle
Bibliographic record
Abstract
Abstract The objective of this project was to examine the effectiveness of feeding a kaolin and yeast-based feed additive on the performance of feedlot cattle. Seventy-five steers (initial BW; 545 ± 42.4 kg) were blocked by BW and randomly allocated into 25 blocks of 3 steers (n = 25 per treatment) in a randomized complete block design with 3 treatments including control, low (10 g/day) and high (15 g/day) kaolin and yeast-based feed additive (FA) supplementation. The steers were fed a finishing diet consisting of 7% corn silage, 1% straw, and 92% corn-based concentrate (DM basis). The duration of the feeding period was 120 days. Supplementation of high grain finishing diet with FA did not affect feed intake (average, 12.4 kg/d), final BW (average, 768 kg), averaged daily gain (average, 1.77 kg/d), carcass traits, and abscessed livers. However, increasing the feeding of FA (0, 10, 15 g/d) to finishing steers linearly (P = 0.05) improved feed efficiency from 0.139 to 0.143 and 0.148 kg gain/kg feed and linearly (P = 0.06) improved net energy for maintenance from 2.05 to 2.10 and 2.15 Mcal/kg and net energy for growth from1.39 to 1.43 and 1.47 Mcal/kg, respectively. Fecal IgA concentration linearly (P = 0.06) increased from 15.0 to 22.7 and 30.1 µg/g with increasing FA addition, suggesting an improvement in nutrient absorption, and potentially better gut health. The increasing addition of FA also linearly (P = 0.03) increased plasma lipopolysaccharide binding protein (LBP) concentration from 46.3 to 56.2 and 64.7 µg/mL but decreased blood total antioxidants from 0.282 to 0.251 and 0.204 mM. These results suggest that the addition of FA may support gut health.
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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 | 0.001 | 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".