Postpartum injection with vitamin E and selenium failed to improve the performance of Awassi ewes and their lambs
Bibliographic record
Abstract
Awawdeh, M. S., Talafha, A. Q. and Obeidat, B. S. 2015. Postpartum injection with vitamin E and selenium failed to improve the performance of Awassi ewes and their lambs. Can. J. Anim. Sci. 95: 111–115. The objective of this study was to investigate the effect of vitamin E and Se injection of nursing Awassi ewes on the performance of ewes (body weight change, milk yield, and composition) and their lambs (growth rate and weaning weight). Twenty-eight Awassi ewes were randomly assigned upon lambing to one of two groups; Control (n=13) and Inject (n=15), where ewes received 0 (control) or 15 plus 0.05 mg kg−1 BW of vitamin E and Se, respectively (inject). Intramuscular injections were given at 1 and 4 wk postpartum. Body weight (BW) of ewes and their lambs were recorded at lambing and at 2, 4, 6, and 8 wk postpartum. Milk yield and composition were measured at 2, 4, and 6 wk postpartum and somatic cell count (SCC) was evaluated weekly (from lambing through 8 wk). Injecting nursing ewes with vitamin E and Se had no effects (P≥0.10) on BW change of ewes, milk yield and composition, composition yields, or milk SCC. Injecting nursing ewes with vitamin E and Se had no significant effects (P≥0.29) on weaning weight, BW gain, or growth rate of their lambs. Under conditions similar to the current study, injecting nursing Awassi ewes with vitamin E and Se at 1 and 4 wk postpartum was not effective in improving the performance of ewes and their suckling lambs. Such observations could be attributed to the time of supplementation (pre- or post-partum) and/or the adequate basal vitamin E status in ewes.
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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 | 0.000 | 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".