Effect of dry or temper rolling of high- or low-protein wheat and its impact on rumen parameters, growth performance, and liver abscesses in feedlot cattle
Bibliographic record
Abstract
This study assessed the impact of dry- (DR) versus temper-rolled (TR) and low- (13%, LP) versus high-protein (18%, HP) wheat on ruminal fermentation, growth, and liver abscesses in feedlot cattle. Crossbred Angus steers (302 ± 34 kg; n = 160; 24 ruminally cannulated) were used in a backgrounding (BG) to finishing (FN) trial. The BG diet consisted of 60% barley silage, 35% wheat, and 5% supplement, and the FN diet contained 10% barley silage, 85% wheat, and 5% supplement (dry matter basis). Four transition (TN) diets were used to adapt cattle to the FN diet. A numerical increase in large particles and reduction in small particles occurred when both HP and LP wheat were TR, with this response being greater for HP wheat. Steers experienced lower ( P ≤ 0.03) ruminal pH with HP-DR and LP-TR than HP-TR wheat during TN. Steers fed HP wheat BG diets tended to exhibit greater ( P ≤ 0.09) gain:feed and NEg than steers fed LP wheat. Greater ( P = 0.01) average daily gains were exhibited by FN steers fed LP wheat. Liver abscesses were more ( P < 0.001) severe with HP wheat. While HP wheat improved the growth of BG cattle, it increased the severity of liver abscesses during FN.
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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".