Leading countries in positive and negative balance of foreign trade in fruit and berry products
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it