Growth performance parameters, carcass traits, and meat quality of lambs supplemented with zinc methionine and (or) zinc oxide in feedlot system
Bibliographic record
Abstract
Zinc (Zn) is a micromineral with structural, catalytic, and regulatory functions in muscle tissue. It improves growth in ruminants because it modulates energy metabolism. The objective of this study was to evaluate the effect of Zn methionine (Zn-Met) and (or) Zn oxide (ZnO) addition in the diet of Katahdin × Dorper lambs on performance, carcass traits, and meat quality characteristics. Forty non-castrated Katahdin × Dorper F1 lambs were randomly assigned to the following experimental groups: (1) Zn-Met (65 ppm), (2) ZnO (65 ppm), (3) Zn-Met + ZnO (32.5 + 32.5 ppm), and (4) basal diet (BD, without Zn). The duration of experiment was 93 d. Treatment Zn-Met + ZnO vs. control improved (P ≤ 0.05) average daily gain (ADG) and feed conversion (FC); ZnO increased leg perimeter and decreased visceral fat. Intramuscular fat (IMF) and marbling of chop with Zn-Met + ZnO vs. control were higher (P ≤ 0.05). Oxidative stability of cooked meat was delayed (P ≤ 0.05) with Zn-Met + ZnO. Myristic acid was lowest (P ≤ 0.05) with ZnO, and arachidonic acid was higher (P ≤ 0.05) in Zn-Met. Therefore, compared with the BD, Zn-Met + ZnO improved the ADG and FC, decreased the shear force, and delayed the stability oxidative in cooked meat; Zn-Met and ZnO increased the IMF and marbling; additionally, Zn-Met increased arachidonic acid and ZnO decreased visceral fat and myristic acid.
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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) | 0.001 | 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 | 0.000 |
| 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".