Use of Holsteins in the process of Lithuania's black-and-white cattle’s selection
Bibliographic record
Abstract
For improving efficiency and technological properties of Lithuania's black-and-white cattle’s, USA and Canada's Holsteins were started to use nearly 30 years ago. Crossing Lithuania's black-and-white cattle with Holsteins, it was indicated that F1, F2, F3 and F4 hybrids, depending on generation and Holstein part in their blood, acquired special dairy cattle type. Hybrid cows, obtained by the method of suppressed crossing or having 50% or more Holstein blood, during 305 days of lactation gave 7,7 – 28,9 % more milk, altogether 3,3 – 22,4 % more milk fat and 5,5 – 22,7 more milk protein, compared to pure-blooded Lithuania's black-and-white cows. However, fat content in hybrid cows' milk was 0,05 – 0,26 % lower, and protein content 0,06 – 0,25 % lower than in the milk of pure-blooded Lithuania's black-and-white cows (except the case of reverse crossing). At high level of feeding conditions, Holsteins also considerably improved milking capacity of Lithuania's black-and-white cows, especially in case when they had 50% or more Holstein blood (4,5 – 17,4%). Fat and protein content in the milk of different generation hybrid cows varied depending on Holstein part in their blood; body mass of hybrids was bigger by 0,5 – 1,6 %. When feeding conditions were worse, milk capacity of cows with Holstein part in their blood was 2,1 – 5,5 % lower; fat content in the milk varied depending on hybrids' generation and part of blood from improoving breed. At intensive selection conditions (minimal efficiency requirement for the first-calf heifers during 305 lactation days was 200 kg of milk fat and 160 kg of milk protein), Lithuania's black-and-white cows and hybrids of all generations gave nearly the same amount of milk with the similar fat and protein content. [...].
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".