Production traits of pigs of intrabreed type “Pripyatsky” in Landrace breed at breeding enterprises
Bibliographic record
Abstract
To meet the population’s demand for meat pork, breeding programs aimed at breeding and selection of pigs with high reproductive, fattening and meat traits were carried out for several decades. Over the years, a number of new types, lines and breeds of pigs were created and tested in the world. It should be noted that the gene pool of foreign super-meat breeds (Pietrain; Duroc; Danish, French, German, Canadian Landrace and Yorkshire) was used to some extent in the development of new domestic meat genotypes of pigs. A rational use of the genetic potential of farm animals allows accelerating the selection to improve their reproductive, fattening and meat traits. The Republic of Belarus arranged its own production of high-value animals represented by intrabreed type-pigs in the Landrace breed with the following productivity indicators: prolificacy – 12.5 animal units, milk yield – 65.5 kg, number of piglets at weaning – 11.6 animal units, litter weight at weaning at the age of 30 days – 91.6 kg, meat content in carcass – 65–67 %, well adapted to the technological conditions of breeding and industrial complexes, providing the overall need of pig breeding in obtaining high-quality pork; saving foreign currency for import; the possibility of exporting finished products to CIS countries.
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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 | 0.001 | 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".