Leading countries in positive and negative balance of foreign trade in fruit and berry products
Bibliographic record
Abstract
In this scientific article, the authors set the goal of identifying the countries that were leaders in terms of positive and negative balances of their foreign trade in fruit and berry products. To achieve this, we determined the difference between the value of exports and imports of goods of this food subgroup for all economies of the world presented in the FAO database for 2014 and 2023. After these author's calculations, twenty countries were selected that were among the top twenty in 2023 for both positive and negative balances. To identify changes that occurred over a ten-year period for each of the selected economies, a comparison of indicators was made relative to 2014. In both compared years, the authors calculated the share of these countries, respectively, in the global positive and negative balance of international turnover of fruit and berry products. Based on the results obtained, two ratings were compiled in tabular form. It was revealed that in 2023, the top ten included Spain, Chile, Thailand, Mexico, Peru, Turkey, South Africa, Ecuador, Brazil, and Costa Rica. Together, they provided 70.10% of the corresponding global positive balance. In the second, the top ten included the following: the United States, China, Germany, Great Britain, Russia, France, Canada, Japan, Switzerland, and Hong Kong. Together, they provided 76.08% of the global negative balance of international turnover of fruit and berry products.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".