Improvement of Kostroma breed cattle with the use of domestic and imported breeding bulls
Bibliographic record
Abstract
The Kostroma breed of cattle of dairy and meat productivity is a valuable gene pool of domestic animal husbandry. Animals of this breed have high milk yields, good meat and reproductive qualities, easily adapt to intensive technologies and are characterized by resistance to tuberculosis, leukemia and brucellosis. The use of the gene pool of brown Swiss cattle of imported origin (USA, Canada, Austria) has a great influence on improving the competitiveness of the Kostroma breed. At the same time, when breeding Kostroma cattle, it is important not only to enrich heredity by using the gene pool of brown Swiss cattle of foreign breeding, but also to preserve its valuable qualities. The monograph presents the history of the creation, characteristics of the modern breeding base of Kostroma cattle in the Kostroma region, the influence of bulls of different breeding on the economically useful qualities of cattle is noted. The analysis of custom-made mating in obtaining successor bulls and optimization of the genealogical structure of the herd, the economic efficiency of using breeding bulls of various origins (Russia, USA, Canada, Austria) are given. It is intended for researchers, specialists of the breeding service, animal breeders of breeding farms, teachers and students of biological and agricultural areas of training and specialties of universities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 0.001 |
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".