Состояние племенной базы мясного скотоводства и дальнейшее совершенствование герефордского скота в Красноярском крае
Bibliographic record
Abstract
Beef cattle breeding is a promising industry which is able to improve its efficiency and increase beef production. The Krasnoyarsk Region has all conditions for beef cattle breeding development. The beef cattle breeding stock of the Region is represented by two multiplication farms: ZAO “Intikulskoye” with the total amount of Hereford cattle of 955 animals, and ZAO “Nazarovskoye” with 508 animals of Hereford and 346 animals of Aberdeen Angus cattle. The research goal was studying conformation and body-composition features of the offspring of Hereford bulls of Siberian and Canadian selective breeding. The research objectives included the evaluation and comparison of conformation and body-composition features of Hereford bulls of Siberian and Siberian-Canadian selective breeding at the age of 24 months. The conventional animal breeding research methodology was used. The experimental research was conducted on ZAO “Intikulskoye” farm. It was revealed that the offspring of the bulls of Canadian breeding were larger and higher compared to those of the bulls of Siberian breeding. They exceeded in most measurements: height at withers and rump by 5.6 cm and 4.7 cm respectively, oblique body length by 8.7 cm, and chest girth behind the shoulders by 10.1 cm. The advantage of conformation features of the bulls of Siberian-Canadian breeding over those of Siberian breeding was proved by conformation indices. The length index was more by 0.9%, leg length index by 0.8%, chest index by 1.3%, and pelvis and chest index by 1.8%. It was concluded that the offspring of Canadian bulls aged 24 months were larger and higher and had greater chest volume than the offspring of Herefords bred in Siberia. When improving the Siberian Hereford herd it is advisable to use the genetic potential of the bulls of Canadian breeding.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".