Effect of leptin genotype and zilpaterol hydrochloride supplementation on the growth rate and carcass characteristics of finishing steers
Bibliographic record
Abstract
McEvers, T. J., Dorin, L. C., Berg, J. L., Royan, G. F., Hutcheson, J. P., Appleyard, G. D., Brown M. S. and Lawrence, T. E. 2013. Effect of leptin genotype and zilpaterol hydrochloride supplementation on the growth rate and carcass characteristics of finishing steers. Can. J. Anim. Sci. 93: 199–204. Steers (n=960; initial body weight=480.2±35.3 kg) were initially selected by leptin genotype (LG; CC=homozygous normal, CT=heterozygous, and TT=homozygous mutant) from a pool of 1500 candidates, and allocated into 48 pens of which one-half were fed zilpaterol hydrochloride (ZH) for 20 d with a 4-d withdrawal and the balance a control ration. No LG×ZH interaction (P≥0.21) occurred for any measured live production or carcass trait. Cattle of the TT genotype tended (P=0.08) to have lower average daily gain (ADG) and gain to feed ratio (G:F) during the pre ZH treatment period. Cattle fed ZH had greater (P<0.01) ADG and G:F than cattle not fed ZH. Cattle of the TT genotype had greater (P<0.01) lipid depots concomitant with reduced (P≤0.02) lean tissue as compared to cattle of the CC genotype; furthermore, TT cattle tended (P=0.07) to have lighter carcass weights than other genotypes. Cattle fed ZH had increased (P<0.01) hot carcass weight and lean tissue concomitant with decreased (P≤0.01) lipid depots. Commercially available leptin genotyping may allow for antemortem sorting of cattle by genotype, which could augment management strategies ultimately leading to adjustments in feeding duration and timeliness of carcass marketing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".