Effects of zilpaterol hydrochloride and zinc methionine on growth performance and carcass characteristics of beef bulls
Bibliographic record
Abstract
Rodríguez-Gaxiola, M. A., Domínguez-Vara, I. A., Barajas-Cruz, R., Mariezcurrema-Berasain, M. A., Bórquez-Gastelum, J. L. and Cervantes-Pacheco, B. J. 2015. Effects of zilpaterol hydrochloride and zinc methionine on growth performance and carcass characteristics of beef bulls. Can. J. Anim. Sci. 95: 609–615. Sixty beef bulls with a body weight (BW) of 314.7±16.2 kg were used to evaluate the effects of zilpaterol hydrochloride (ZH) and zinc methionine (ZM) on growth performance and carcass characteristics. The experimental design was a randomized complete block, with a factorial 2×2 arrangement of treatments (ZH: 0 and 0.15 mg kg−1 BW; ZM: 0 and 80 mg kg−1 dry matter). The ZH increased (P<0.05) the final BW, average daily gain, feed conversion, carcass yield and longissimus dorsi area. Bulls fed ZH plus ZM had less (P<0.01) backfat thickness and intramuscular fat (IMF) compared with those fed ZH or ZM alone. The ZH increased (P<0.02) the meat crude protein content and cooking loss. It is therefore concluded that ZH increases growth performance, carcass yield, longissimus dorsi area, and meat crude protein. The interaction of ZM and ZH did not present additional advantages. The reason for the reduction in backfat thickness and IMF by ZH plus ZM is unclear, and implies that our knowledge of β-agonistic adrenergic substances and their interactions with minerals is incomplete.
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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 | 0.000 |
| 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".