Dietary urea, exogenous estradiol-17β, and nitrogen utilization in Holstein steers fed a low-protein diet
Bibliographic record
Abstract
The Cornell Net Carbohydrate and Protein System model was used to formulate a low-protein mixed grass hay and corn diet predicted to create a ruminal N deficiency of 33% in 250-kg Holstein steers. Nitrogen metabolism, digestibility and metabolic status responses were compared between this control diet and a similar diet supplemented with 1.7% urea to compensate for the ruminal N deficiency. A 4 × 4 Latin square design was used to analyze main effects of diet and subcutaneous administration of 500 μg estradiol-17β (E2) twice a day. Urea supplementation increased N intake from 60 to 93 g d-1, improved N balance from 10.1 to 17.7 g d-1, and improved total tract digestibility of N, neutral detergent fiber (NDF), organic matter (OM), and dry matter (DM) (all P < 0.05), but there was no effect of urea supplementation on total tract digestibility of non-structural carbohydrate (NSC) and N retention (percent of N intake). Plasma urea N increased fourfold (P < 0.05) and plasma insulin increased from 0.32 to 0.50 ng mL-1 (P = 0.06) when the urea diet was fed. Administration of E2 did not alter N metabolism or plasma metabolites and insulin at either level of protein intake. It is concluded that supplementing a fiber-rich grass-hay-based diet with urea to achieve ruminal N balance increases digestibility of fiber fractions without altering dietary N utilization. Under these nutritional conditions the use of estrogenic growth promoters remains ineffective independent of ruminal N balance. Key words: Steers, nutrition, fiber, urea, estradiol, nitrogen balance
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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) | 0.001 | 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) | 0.000 | 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".