Genetic parameter estimates for ultrasound-measured carcass traits in sheep
Bibliographic record
Abstract
The purpose of this thesis was to estimate genetic parameters for ultrasound-measured carcass traits in a multi-breed sheep population. Field data collected between 1997 and 1999 from 26 producers across Ontario were used in the analysis. Data were collected for three measurements: loin depth, backfat depth and loin width using the Ultrascan 50. Genetic and phenotypic parameters including heritability, genetic and phenotypic (co)variances and resulting correlations were estimated assuming an animal model. Heritabilities were estimated as 0.29, 0.29 and 0.26 (weight-constant) and 0.38, 0.35 and 0.30 (age-constant) for the traits loin depth, average backfat thickness and loin width respectively. Genetic improvement in carcass traits can be made through selection based on these ultrasound-measured traits. Data were also collected from an experiment with 38 Rideau-Arcott X Dorset lambs from the New Liskeard Agricultural Research Station to examine the accuracy of the Ultrascan 50 to measure tissue depth. Repeated ultrasound measurements were recorded for a total of 7 replicates per trait per animal. Within and between animal variances were calculated using ANOVA. Pearson correlation of 0.93 for loin depth was calculated between the ultrasound-measured trait and the same measurement on the carcass. Ultrasound-measured traits should be a valuable tool in improving meat quality in the sheep industry based on the results of this study.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".