Effects of a beta-adrenergic agonist (L-644,969) on performance and carcass traits of growing lambs in a cold environment
Bibliographic record
Abstract
A 2 × 2 factorial experiment was conducted to study the effects of the beta-adrenergic agonist L-644,969 (BAA, supplied at 0.28 vs. 0 mg kg−0.75 d−1) on growth performance, carcass traits and nitrogen balance of lambs at two environmental temperatures (0°C vs. 20 °C). Sixteen Suffolk-Cross wether lambs (30.2 ± 2.06 kg BW) were randomly divided into four groups and exposed to each of four experimental treatments (20 °C with control diet; 20 °C with BAA-supplemented diet; 0 °C with control diet; and 0 °C with BAA-supplemented diet) for 5 wk. The collection period for nitrogen balance was 4 d during the 4th week of treatment. The low temperature decreased efficiency of feed utilization and increased backfat thickness by 20.8% (P < 0.01) and 35.1% (P < 0.05), respectively. L-644,969, on the other hand, increased daily gain, feed efficiency and hot carcass weight by 22.5% (P < 0.05), 17.2% (P < 0.05) and 9.6% (P < 0.05,) respectively. Dressing percentage (P < 0.01), biceps femoris weight (P < 0.01) and rib eye area (P < 0.01) were also increased, but abdominal fat expressed as a percentage of liveweight (P < 0.05) was significantly decreased by BAA treatment. The improved weight gain, muscle weight and ribeye area in response to BAA, and in the absence of an increase in total nitrogen retention, indicate that the responses are due to nutrient repartitioning. Temperature did not impair the nutrient repartitioning effect of BAA, but feed efficiency may be more improved by BAA for the animals in the cold environment. Key words: Performance, carcass traits, beta-agonist, temperature, lambs
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".