Livestock of specialized beef cattle breeds, state of beef production and industrial use of cows in Russia (review)
Bibliographic record
Abstract
Purpose: to analyze the livestock population of the main beef breeds, the state of beef production and the industrial use of cows. Materials and methods. The methodological basis of the study was made up of systematization techniques, logical and comparative statistical analysis. The analysis was carried out using domestic and foreign bibliographic databases, yearbooks on breeding work in beef cattle breeding in farms of the country. Results. Regardless of the analysis period, the largest number of livestock was observed in the Kalmyk breed, the proportion of which amounted to 30,5—33,8 % of all probonitized cattle of beef breeds and increased by 3,3 abs. % compared to 2022. The number of Aberdeen Angus cattle in 2022 amounted to 26,5 % of the total number of beef breeds, while a year later it decreased to 16,9 %, which was less than the number of all other studied breeds. With the exception of Aberdeen Angus, the number of probonitized livestock of Kalmyk, Kazakh White-Headed and Hereford breeds increased, although slightly. Of all the analyzed beef cattle breeds, Aberdeen Angus cattle had an earlier age at the first insemination, with values of 16 months, followed by representatives of the Kalmyk breed (23,8 months), which indicates the late maturity of these animals. The remaining breeds were inseminated in the period of 18—20 months and occupied an intermediate position between the extreme values of the trait. In terms of age of withdrawal from the herds, the best values were shown by cows of the Kalmyk and Kazakh White-Headed breeds, which averaged 6,8—6,9 calvings. Herefords were characterized by a lower age of cow retirement among the studied breeds, the values of which were 0,5—1,3 calvings lower. A low level of calf output per 100 Aberdeen Angus cows was revealed — 56 heads, which is lower than the indicators obtained from other breeds of meat productivity by 27— 29 heads. Conclusion. The results obtained make it possible to better assess the growth and changes in the linear weight and exterior parameters of fish during cultivation, and are also of great practical importance when carrying out breeding work with rainbow trout of the Kamloops breed.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".