The use of linear evaluation of the conformation of the daughters of sires in breeding work
Bibliographic record
Abstract
Breeding work aimed at improving the body type of cattle is of a big importance for improving the efficiency of dairy cattle breeding, since harmoniously built animals are characterized by high milk productivity, long-term economic use and are in significant demand on the market of breeding products. Linear evaluation of the body type of dairy cattle has become very popular and is widely used to assess the appearance of animals in many countries with highly developed cattle breeding (USA, Canada, Netherlands, Germany, etc.). Many researchers use a linear evaluation method for evaluating the body type of cows of different origins obtained both by crossbreeding with improving breeds, and for evaluating sires on the quality of offspring. In the Omsk region scientific research on the use of linear evaluation of the body type of daughters of sires has been carried out in order to further assess their breeding qualities. The purpose of the research was to study the conformation features of first-calf heifers have been obtained from different sires evaluated using the method of linear evaluation of body type. The experimental part of the work has been carried out in breeding farms in the Omsk region: JSC “Razdolnoe” of the Russian-Polyansky district and JSC “Azovskoye” of the Azov district. It has been established that the sire directly determines the conformation features of the daughters, which in turn affects their milk productivity, health and ease of calving.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".