Bibliographic record
Abstract
The article is devoted to the main trends in soybean production in the world. The paper analyzes the dynamics and structure of sown areas, gross harvest volumes and soybean yields in the world for the period 2014- 2023 in the context of leading producing countries. The increase in sown areas and increased yields ensures the growth of global soybean production. In 2023, the global gross soybean harvest amounted to 398.2 million tons, of which 1.71% was produced in Russia. According to the current structure of the soybean sown area in the world, almost 80% is concentrated in three main leading countries - Brazil, the USA and Argentina. Based on statistical data from the U.S. Foreign Agricultural Service, The Department of Agriculture (USDA) conducted a ranking of the yield level, thereby identifying the main leading countries with the highest yield level - Turkey (41.2 c/ha), the United States and Brazil (34.0 c/ha), and Canada and Argentina - (30.9 c/ha) and (30.3 c/ha), respectively. The dynamics and structure of domestic soybean consumption confirms the importance and uniqueness of soybean as one of the main agricultural crops in the world. As a result of the study, the main global trends in soybean production were identified.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| 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) | 0.002 | 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".