ЭФФЕКТИВНОСТЬ ИСПОЛЬЗОВАНИЯ БЫКОВ-ПРОИЗВОДИТЕЛЕЙ РАЗЛИЧНОЙ СЕЛЕКЦИИ ПРИ РАЗВЕДЕНИИ СКОТА КОСТРОМСКОЙ ПОРОДЫ
Bibliographic record
Abstract
Представлен материал по эффективности использования быков-производителей различной селекции при разведении скота костромской породы. В племенных организациях удой коров по данным бонитировки в 2018 году составил 6623 кг, содержание жира 4,23, белка 3,32, живая масса 548 кг. Наряду с быками отечественной селекции, при совершенствовании породы используют бурый швицкий скот из США, Канады и Австрии. Изучены методы получения быков-производителей различной селекции и их оценка по качеству потомства. Большинство быков были получены при использовании кроссов линий (74,1), из которых быки-улучшатели составили 55,0. На основании родительского индекса определена реализация генетического потенциала коров костромской породы. Проанализирована молочная продуктивность коров-первотелок в зависимости от разной кровности по улучшающей породе и селекции. Рассчитана рентабельность производства молока коров-первотелок разного происхождения. The article deals with the research results of use efficiency of stud bulls where multiple selection in the Kostroma cattle breed is used. In the year 2018 milk yield was 6623 kg on breed livestock farms, fat content 4,23, protein content 3,32, body weight 548 kg. Domestic stud bulls as well as Brown Swiss from the USA, Canada and Austria are used in the selective breeding. The methods of stud bull production in the multiple selection and progeny records are studied. Most of the bulls were produced by means of line-crossing (74,1), 55,0 of them were bull-improvers. The genetic potential of the Kostroma breed at stud breeding is determined. Milk production of first-calf cows depending on pedigree and selection is analyzed. Milk production efficiency of first-calf cows received in the multiple selection is calculated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.011 |
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".