Changes in international trade in exotic tropical fruit
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 a goal to explore changes in the volume and structure (by country) of international trade in exotic tropical fruits over 2011-2022. Based on the statistical database of the UN Food and Agriculture Organization (FAO), we found that during this period, global exports increased by 2.12 times (from 646.342 thousand tons to 1371.977 thousand tons), and imports by 1.96 times (from 811.232 thousand tons to 1593.386 thousand tons). The authors compiled a rating of countries that in 2022 were in the top ten in terms of natural parameters of international trade in these types of fruit and berry products, and found absolute and relative changes in their corresponding indicators relative to similar ones for 2011. We found that in 2022 in the top five in exports exotic tropical fruits were represented (given that China and Hong Kong are considered separately in FAO statistics) Thailand, Hong Kong, Vietnam, Egypt and China. Together, these countries contributed 90.37% of the corresponding global figure, with Thailand accounting for 60.3%. The authors determined that the top five imports of exotic tropical fruits in 2022 were China, Hong Kong, Canada, Russia and Singapore. Together, these countries contributed 89.29% of the corresponding global figure, with China (together with Hong Kong) accounting for 80.87%. This indicates a fairly high concentration of international trade in this category 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) | 0.001 | 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