A genetic evaluation of growth, ultrasound, and carcass traits at alternative slaughter endpoints in crossbred heavy lambs1
Bibliographic record
Abstract
Genetic parameters were estimated for growth, ultrasound, and carcass traits in a Canadian crossbred heavy lamb population. Traits analyzed included birth, weaning, post-weaning, and ultrasound scanning weights; pre- and post-weaning average daily gain; ultrasonically measured eye muscle and fat depths; hot carcass weight; fat depth at the GR site (110 mm from the midline on the 12th rib); carcass conformation scores; saleable meat yield; price grid value; and total carcass value. The impact of three alternative slaughter endpoints (slaughter age, carcass weight, and carcass fatness) on genetic parameter estimates was also evaluated. In general, carcass traits were found to be moderately heritable, with heritability estimates ranging from 0.17 ± 0.02 for hot carcass weight at a constant slaughter age to 0.34 ± 0.02 for average carcass conformation score at a constant carcass weight. Heritability estimates were similar when observations were adjusted to alternative slaughter endpoints, but for some traits, phenotypic variance and genetic correlation estimates differed. Genetic correlations between carcass traits and growth and ultrasound traits were typically favorable. Ultrasonically measured eye muscle depth and fat depth were found to be moderately to strongly positively correlated with hot carcass weight (0.33 ± 0.15 to 0.71 ± 0.19) and fat depth at the GR site (0.38 ± 0.14 to 0.74 ± 0.12), respectively, reaffirming the usefulness of selection on ultrasound traits to improve carcass yield and quality. Genetic correlations among carcass traits were generally favorable, with the exception of moderate unfavorable positive genetic correlations between fat depth at the GR site and primal cut carcass conformation scores (0.31 ± 0.05 to 0.60 ± 0.05). Overall, the results of this research suggest that there is potential to improve carcass yield and quality through genetic selection and provides the population-specific genetic parameter estimates needed for the genetic evaluation of carcass traits in the Canadian sheep population. Nevertheless, the optimal endpoint for carcass trait genetic evaluations will need to be further investigated, considering both the current findings and additional information on production practices in the industry.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".