PSX-19 Factors affecting growth and carcass trait performance of Canadian heavy lambs
Bibliographic record
Abstract
Abstract The profitability of meat lamb production is strongly dependent on growth and carcass trait performance of market lambs. The objective of this research was to test the significance of non-genetic factors on growth, ultrasound, and carcass traits of Canadian heavy lambs. Hot carcass weight (HCW, kg), fat depth at the GR site (FATGR, mm), average carcass conformation score (CONF, points), and total carcass value (PRICE, $CAD) were measured for 8,865 purebred lambs marketed through Quebec’s Heavy Lamb Sales Agency. Corresponding management information and growth trait records for over 19,000 animals with carcass records and their relatives were extracted from the Canadian Sheep Genetic Evaluation System. Single-trait mixed linear models in SAS were used to test the significance (P < 0.05) of various non-genetic effects, after a Scheffe adjustment for multiple comparisons. All models included categorical fixed effects of sex (male or female), breed (Hampshire, HA; Suffolk, SU; Canadian Arcott, CD; Polled Dorset, DP; Rideau Arcott, RI; Polypay, PO), dam age at parity (1 to 7+ years), and birth and rearing type (born as single, twin, or triplet and more, and reared as single or multiple), and a random effect of contemporary group. Linear covariates of slaughter age or carcass weight were included in the carcass trait models, while a scanning weight covariate was used for ultrasound trait models. Male lambs were found to be significantly heavier during growth, had greater HCW and PRICE, and lower FATGR and CONF than female lambs. As expected, terminal breeds (HA, SU, CD) tended to have greater growth, greater HCW and PRICE, and lower FATGR than maternal (DP, RI, RV, PO) breeds. This information could be utilized by Canadian sheep producers to manage their flocks to maximize the revenue of lambs marketed through price grid classification systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".