Comparison of fertility and yield of Suffolk sheeps in the Czech Republic with some foreign Suffolk sheeps managements
Bibliographic record
Abstract
Suffolk breed is the English hornless breed which belongs to the breed of meat yield. Typical features are excellent maternal qualities, good milkiness of ewes and fertility, less fertile period (lambing mostly in winter and spring) and good adaptability to different climatic conditions and breeding conditions. The breed is characterized by a black outer coat on the face of the head and lower part of legs, wool is short, white, semi-fine. Suffolk breed is characterized by high fertility throughout the production period ewes. Season, age, interval among lambing, body weight and body conditions score, nutrition, genetics, breeding and heat stress belong to the factors affecting fertility. When we compare fertility sheep breed Suffolk in the Czech Republic and Slovakia we achieved a higher number of ewes, improved fertility and fertilization. On the contrary, Slovakia has achieved better results in fertility at lambing ewe. It was also achieved in Canada in comparison with our republic. It is a major prerequisite of the production of heavy great muscled slaughter lambs with very good quality meat at meat production. Meat production is influenced by hormones, nutrition, gender, influence year and month of lambing, ewes age, litter size on the dependent variable. The values are not very different when meat production in Suffolk breed is evaluated in the Czech Republic and Slovakia. In spite of it, the Czech Republic achieves better results in weight of lambs at 100 days and the average daily gain. If we evaluate the difference between meat production in Canada and the Czech Republic in selected parameters such as birth weight of lambs, weight of lambs at 100 days of age and avarage daily gain, Canada clearly has much better results. The main cause is a different type of breed. Increase fertility and meat production can be achieved mainly by improving reproduction and production indicators.
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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 | 0.000 | 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".