Evaluation of sheep genetic resources in North America: Lamb productivity of purebred, crossbred and synthetic populations
Bibliographic record
Abstract
Lamb weights and daily gains from divergent genetic types of established purebreds, e.g., Dorset (D), Lincoln (L), Rambouillet (Ra), Suffolk (Su) and Targhee (T), and fecund-type breeds, e.g., Finnsheep (F) and Romanov (Ro), their reciprocal crosses and Suffolk sired specific cross Su(F × Ro) were evaluated. Also evaluated were lambs of the Outaouais (O) and Rideau (R) Arcott breeds and their reciprocal crosses, in addition to Synthetic I (½ F, ½ L), Synthetic II (½ D, ½ Ra) and Synthetic III (¼ F, ¼ L, ¼ D, ¼ Ra) populations. The established purebreds produced heavier lambs at birth and weaning, Arcott breed crosses gained weight more rapidly resulting in heavier lambs at 140 d, and fecund-type breeds produced lighter lambs (P < 0.05). In general, daily gains and lamb weights of all genetic types were similar, except that fecund-type breeds produced significantly lighter lambs. Lamb weights of T were most at birth, and Su at weaning and 140 d, while F lambs weighed the least (P < 0.05). Within established purebreds, Su weighed the most and D weighed the least, while L, Ra and T lambs were intermediate. Daily gains including weaning and 140-d weights of F and R cross lambs benefited from 7–9% heterosis, while the Arcott breed cross lambs not only benefited from 5–8% heterosis, but were comparable with Su lambs. At the same time, lamb performance of Su(F × Ro) was similar to the average of their parental breeds. Lambs of synthetic populations relative to the average of their respective parental breeds weighed 8–24% more at 140 d, suggesting heterosis retention. Key words: Growth, North American breeds, Finnsheep, Romanov, Arcotts, synthetic populations
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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 | 0.001 | 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 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".