Analysis of the current positions of pea crop in the Russian market
Bibliographic record
Abstract
Abstract The paper considers the current situation in the Russian Federation of such a crop as peas. Data on the area of crop cultivation is presented not only in the world, but also in the context of Federal districts and regions. In the world, the main producers of peas are countries such as Canada, Russia, China and India. In the Russian Federation, the main areas under crop are located in the Volga, Siberian, Southern and Central Federal districts. They are also leaders in the gross harvest of pea grains. It also shows data on the yield of peas sown in the leading regions of Russia for several years, which allows assessing more accurately the value of this indicator. So the highest yield of peas is obtained in the Oryol and Kursk regions, as well as in the Krasnodar territory. The dynamics of inclusion of pea breeds in the State register of breeding achievements allowed for use is analyzed. As before, national breeds prevail over foreign ones in terms of the total number in the register, but producers prefer Western European varieties to a greater extent. Due to its self-sufficiency in peas, Russia is an exporter to countries such as Spain, India, Turkey, Italy, etc. The reasons for the low competitiveness of national breeds are indicated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".