Bibliographic record
Abstract
The purpose of the analysis presented in the article is to study the dynam-ics of export deliveries of Russian wheat abroad in 2020-2024, to identify and assess the ongoing political and economic changes, and their possible impact on the domestic market and global food stability. General scientific approaches were used as the methodological basis of the study: analysis, comparison and generalization of data related to export changes in Russian grain supplies in the period from 2020 to 2024, as well as the study of available statistical information on the activities of leading grain companies in Russia engaged in foreign trade activities. It was established that in the 2023-2024 agricultural year, grain exports abroad from Russia reached 55.5 million tons, which gave it a quarter of the world wheat market. At the same time, according to expert forecasts, in the up-coming 2024-2025 marketing season, export volumes may decrease to 48 million tons, and the country's share in global trade will decrease to 22.5%. It was re-vealed: recently, there has been a reduction in the list of countries buying Russian wheat, but export volumes have increased significantly to some countries, espe-cially to the BRICS countries, including Brazil, China and Saudi Arabia, which maintained the leading position of the Russian Federation in the world market in 2024 (second place in grain sales). It was determined: in 2021-2022, the number of exporters to the Russian Federation decreased to 230 grain traders, mainly due to the geopolitical situation. But already in the 2022-2023 season, their growth to 266 exporting companies was recorded.
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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 | 0.001 |
| 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) | 0.003 | 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".