The effects of ovariectomy on meat performance and expression of GH/IGF-I in young goats
Bibliographic record
Abstract
Zhang, L., Wang, Y.-y., Fu, M.-z., Li, G., An, N., Li, S.-y. and Zhou, Z.-q. 2014. The effects of ovariectomy on meat performance and expression of GH/IGF-I in young goats. Can. J. Anim. Sci. 94: 619–626. Experiments were carried out to investigate the effects of ovariectomy on meat production efficiency and to explore the expression of GH/IGF-I in young goats. Animal performance, meat quality, levels of serum growth hormone (GH) and insulin-like growth factor 1 (IGF-I), and mRNA levels of three key genes [GH Receptor (GHR), IGF-I and IGF-I Receptor (IGF-IR)] in longissimus dorsi and biceps femoris muscles were measured. The results show that carcass weight, net meat mass, fat weight and loin eye area of ovariectomized goats were higher than those of the controls, and ovariectomized goats lost 0.40 kg of bone weight (P<0.05). There was no statistically valid difference for the color, pH, water-holding capacity, or cooking rate of meat (P>0.05) between the two groups, except for the shear value, which was significantly lower in the Ovx group than in the control group (P<0.05). The results of this research show for the first time a significant trend (P<0.05) for serum GH and IGF-I in the direction of increasing in ovariectomized goats. Furthermore, the mRNA levels of GHR, IGF-I and IGF-IR in muscle were all up-regulated, except for the IGF-I gene in biceps femoris, by ovariectomy. In summary, ovariectomy showed a beneficial promotion in animal performance, but did not reduce meat quality, and increased serum GH and IGF-I and mRNA expression levels of GHR, IGF-I and IGF-IR in young female goats.
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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 | 0.001 |
| 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".