PSI-20 Effects of physical exercise on growth performance, and carcass and meat quality characteristics of Sunit sheep
Bibliographic record
Abstract
The objective of this study was to investigate the effects of muscle exercise on growth performance, proteins and genes expression, and carcass and meat quality characteristics of Sunit sheep. To this end, 24 Sunit sheep with same genetic background that were raised on extensive pasture for their first 9 months of life were randomly distributed into two groups (12 sheep each) and raised separately for 3 months until slaughter. The first group of sheep was kept on pasture and allowed to move freely outdoors (M Group), while the other group of sheep was kept in one pen inside the farm (control group or C Group). After slaughter, carcass characteristics were assessed and samples of the longissimus dorsi (LD) and biceps femoris (BF) muscles were taken to evaluate meat quality. Furthermore, the differences in the expression of muscle exercise-related proteins and genes between the two groups were analyzed by Western blotting and RT-PCR techniques. Results showed that growth traits, such as body length and height, and chest circumference and weight before slaughter were higher (P<0.01) in the M Group than in the C Group, while the lean and carcass yields were higher in C Group (P<0.05). A reduction of the pH1, pH24and colour a∗ and b∗ values was recorded in the LD muscle of the M sheep compared with C ones (P<0.01).These results may be associated with the greater (P<0.05) level of AMPK RNA expression, indicator of muscle glycolytic potential (GP), in the M group. The BF muscle of M sheep was tender (as assessed by shear force) than that of C sheep (P<0.01). In conclusion, allowing sheep a long-term exercise before slaughter can improve their growth rate, but at detriment of carcass yield.
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) | 0.000 | 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".