The Assessment of Türkiye’s Competitiveness in Cherry Trade
Bibliographic record
Abstract
Cherries are a fruit that thrives in warm climates and has a robust presence in the global market. As cherry production areas expand, there is a corresponding increase in demand for this fruit worldwide. The trade of agricultural products has gained importance with the development of global trade. Cherries have a crucial place in Turkish agricultural exports. Fresh cherries are in the scope of this study. This study examines Türkiye's position in the global cherry trade, specifically in the fresh cherry market. Türkiye is a leading country in cherry production and plantation area and has seen an average annual export growth of 18.4% between 2001 and 2020. However, Türkiye's share in the world cherry market has decreased from 14% to 5.9% due to low unit export value. Chile, the USA, Hong Kong, Austria, Spain, and Canada are significant cherry exporters, with Chile dominating the market with a 72.5% share in 2020. The Trade Intensity Analysis Method which represents the course of trade flow among countries is used in the study. The study indicates that European countries which are Türkiye’s traditional trade partner has the biggest trade share with Türkiye and that share did not change much over the years. By the way, the Asia market especially China, became a game-changer in cherry trade and Türkiye should prepare itself for this situation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".