The leptin arg25cys affects performance, carcass traits and serum leptin concentrations in beef cattle
Bibliographic record
Abstract
A single nucleotide polymorphism (SNP) in the bovine leptin gene has been associated with carcass traits and elevated gene expression. To examine the relationship between leptin genotype with serum leptin concentration and carcass traits, blood samples were collected 24 h prior to slaughter in 89 head of cattle. Cattle were predominantly of Angus (n = 26), Hereford (n = 31) and Charolais (n = 32) breed types with approximately half homozygous for the T allele or the C allele. Cattle were limit fed to achieve 1 kg d-1 liveweight gain for a 70-d background period while during finishing, animals were fed ad libitum such that half the animals within each breed type were slaughtered at 8 or 12 mm back fat determined by ultrasound. Preslaughter serum leptin and insulin were determined using radioimmunoassays (RIA) specific for cattle and sheep. Animals homozygous for the T allele had greater (P < 0.05) backfat depth at the beginning (2.4 ± 0.49 vs. 1.8 ± 0.49 mm) and end (3.1 ± 0.42 vs. 2.4 ± 0.42 mm) of the backgrounding period and reached target finishing back fat depths at lighter (P < 0.01) weights (548.2 ± 20 vs. 588.0 ± 20 kg) and in fewer (P < 0.05) total days on feed (179.3 ± 13 vs. 195.2 ± 13 d) than homozygous C animals. Leptin genotype effects on serum leptin concentration were confined to a three-way interaction such that TT Charolais fattened to 12 mm had significantly higher serum leptin levels than CC animals. Leptin concentration was correlated positively with measurements of fat (e.g., average ultrasound fat depth at end of test r = 0.45, P < 0.01). Results are consistent with an increased rate of fat deposition associated with the T allele in leptin. Key words: Beef cattle, leptin, carcass, yield
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".