Growth performance, carcass characteristics, and meat quality of Mongolian lambs fed native grass or hay with or without concentrate on the Inner Mongolian Plateau
Bibliographic record
Abstract
The aim of this study was to evaluate the effects of native grass on growth performance, carcass characteristics, and meat quality of Mongolian lambs fed Inner Mongolia native grass in three forms: grass, hay, and hay with concentrate (HC). Sixty Mongolian noncastrated male lambs, in good health and with the same age (6 mo) and similar body weights (mean, 28.63 ± 0.19 kg), were randomly divided into three treatments. There were four replicates in each treatment, with five lambs in one pen as a replicate. Compared with the HC group, the intake of the grass group was higher (P < 0.05) and that of the hay group was lower (P < 0.05). The hay group had a gain of −58.67 g d−1, compared with 42.33 g d−1 for the HC group and 80.00 g d−1 for the grass group. The carcass weight, net meat mass, loin muscle area, and fat thickness (a measure of fat tissue thickness) were greater in the grass and HC groups than in the hay group (P < 0.05). Compared with the hay group, the protein and calcium contents, marbling score, and water loss rate were greater in the grass and HC groups (P < 0.05), whereas the fat, phosphorus, cholesterol contents, and b* score were lower for the latter groups (P < 0.05). These results suggest that feeding of native grass hay with concentrate could improve the growth performance and meat quality of Mongolian lambs and achieve results similar to those observed in grass-fed 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) | 0.000 | 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) | 0.000 | 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".