Effects of dietary urea concentration on performance and health of receiving cattle and performance and carcass characteristics of finishing cattle
Bibliographic record
Abstract
Effects of urea concentration for receiving and finishing cattle were examined. In exp.1, 197 newly received beef steers (188 kg) were used, and treatments included 0, 0.5, or 1.0% urea [dry matter (DM) basis] in a 70 or 75% concentrate (steamflaked corn-based) diet. A quadratic response (P < 0.05) was observed for dry matter intake (DMI) of concentrate and total DMI during days 0 to 14 with DMI lower for 0.5% urea. A quadratic (P < 0.10) increase in gain:feed for 0.5% urea was noted during days 15 to 28 and days 0 to 28. Urea concentration did not affect bovine respiratory disease (BRD) morbidity. In exp. 2, 235 yearling beef steers (379 kg) and 126 yearling beef heifers (346 kg) were used to evaluate 0, 0.5, 1.0, 1.5, or 1.75% urea concentrations in a steam-flaked sorghum grain-based diet. Average daily gain (ADG) (1.44, 1.48, 1.51, 1.47, and 1.43 kg for 0, 0.5, 1.0, 1.5 or 1.75% urea, respectively), DMI (9.4, 9.3, 9.6, 9.4, kg and 9.2 for 0, 0.5, 1.0, 1.5 or 1.75% urea, respectively), and gain:feed (0.153, 0.160, 0.157, 0.157, and 0.157 for 0, 0.5, 1.0, 1.5 or 1.75% urea, respectively) did not differ (P > 0.10) among treatments for the overall experiment. No major differences were noted for carcass characteristics. Optimum level of dietary urea for newly received beef cattle fed 70 to 75% concentrate diets is approximately 0.5% of the DM for maximum feed efficiency and added urea concentrations did not alter performance or carcass characteristics to a great extent with steam-flaked sorghum grain-based finishing diets. Key words: Beef cattle, urea, health, performance, carcass quality
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".