Bibliographic record
Abstract
В статье рассмотрен зарубежный опыт функционирования рынка мяса. На мировом рынке производства животноводческой продукции лидирующие позиции занимают США, Канада, страны Северной Европы, Австралия, Новая Зеландия. В зарубежных странах данная подотрасль животноводства характеризуется интенсивным развитием, а крупный рогатый скот мясного направления является основным источником говядины, производимой в стране. Это объясняется природноклиматическими условиями при ведении животноводства, освоением новых технологий, повышением продуктивности животных. В ведущих странах производителях говядины крупный рогатый скот мясных пород составляет 60-90% от мясных пород. The article considers the foreign experience of the functioning of the meat market. The USA, Canada, the Nordic countries, Australia, and New Zealand occupy leading positions in the world market of livestock production. In foreign countries, this sub-sector of animal husbandry is characterized by intensive development, and beef cattle are the main source of beef produced in the country. This is due to the natural and climatic conditions in animal husbandry, the development of new technologies, and increased productivity of animals. In the leading beef-producing countries, beef cattle account for 60-90% of meat breeds.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".