The main countries of the world in terms of value exports and imports of fruit and berry products
Bibliographic record
Abstract
The objective of this research article was to consider the parameters of the value export and import of fruit and berry products in the countries of the world that are among the main ones in these areas of international trade. Based on FAO data, the authors identified twenty countries that were leaders in these indicators by the end of 2023. In order to identify the changes that occurred over a ten-year period for each of the selected countries, a comparison of the indicators was made relative to 2014. In both compared years, the authors calculated the share of these countries in the global export and import of fruit and berry products. Based on the results obtained, two ratings were compiled in tabular form. The authors found that in 2023, the top ten in terms of export of goods of the designated food subgroup included Spain, the Netherlands, the USA, Thailand, China, Chile, Mexico, Italy, Turkey, and Peru. Together, they provided 52.12% of the corresponding global volume. The second ten included South Africa, Belgium, Brazil, Ecuador, Vietnam, Germany, Costa Rica, Poland, France, Egypt. The following ten countries were leaders in imports of fruit and berry products: the USA, China, Germany, the Netherlands, France, Great Britain, Canada, Russia, Japan, Spain. In total, they provided 61.55% of the corresponding global volume. The second ten included Belgium, Italy, Poland, Hong Kong, Vietnam, Austria, the UAE, Saudi Arabia, Mexico, Indonesia. Some of the countries are present in both ratings, since part of the fruit and berry products are sent through them to other countries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".