Effect of combinations of feed-grade urea and slow-release urea in a finishing beef diet on fermentation in an artificial rumen system
Bibliographic record
Abstract
Abstract This study evaluated the effect of combinations of feed-grade urea and slow-release urea (SRU) on fermentation and microbial protein synthesis within two artificial rumens (Rusitec) fed a finishing concentrate diet. The experiment was a completely randomized, dose–response design with SRU substituted at levels of 0% (control), 0.5%, 1%, or 1.75% of dry matter (DM) in place of feed-grade urea, with four replicate fermenters per dosage. The diet consisted of 90% concentrate and 10% forage (DM basis). The experiment was conducted over 15 d, with 8 d of adaptation and 7 d of sampling. Dry matter and organic matter disappearances were determined after 48 h of incubation from day 9 to 12, and daily ammonia (NH3) and volatile fatty acid (VFA) production were measured from day 9 to 12. Microbial protein synthesis was determined on days 13–15. Increasing the level of SRU quadratically affected total VFA (Q, P = 0.031) and ammonia (Q, P = 0.034), with a linear increment in acetate (L, P = 0.01) and isovalerate (L, P = 0.05) and reduction in butyrate (L, P = 0.05). Disappearance of neutral detergent fiber (NDF) and acid detergent fiber (ADF) was quadratically affected by levels of SRU, plateauing at 1% SRU. Inclusion of 1% SRU resulted in the highest amount of microbial nitrogen associated with feed particles (Q, P = 0.037). Responses in the efficiency of microbial protein synthesis fluctuated (L, P = 0.002; Q, P = 0.001) and were the highest for 1% SRU. In general, the result of this study showed that 1% SRU in combination with 0.6% urea increased NDF and ADF digestibility and total volatile fatty acid (TVFA) production.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".