OPTIMAL DISTRIBUTION OF EGYPTIAN WHEAT IMPORTS IN LIGHT OF THE RUSSIAN-UKRAINIAN WAR
Bibliographic record
Abstract
The research mainly aimed to study the current situation of Egypt’s import of wheat from Russia and Ukraine in light of the events of the current war, and to identify the current geographical distribution of Egypt’s wheat imports with the aim of re-distributing geographically in order to reduce the value of those imports, and not rely on a specific market. The research found a statistically significant increase in both productivity and area of the wheat crop in Egypt, while production was characterized by relative stability during the study period. It was also shown that the most important countries exporting to Egypt are Russia, Ukraine, Romania, France, America, and Australia, which together represent about 97.5% of Egypt’s total wheat imports during the period (2018-2022). It also became clear that these countries are also the most important wheat exporting countries in the world, in addition to Canada, Argentina, and Germany. By studying the impact of the Russian-Ukrainian war on Egypt’s wheat imports, it was found that it led to an increase in both food inflation prices and Egypt’s import prices from Ukraine. It also led to a decrease in the amount of Egypt’s imports from Ukraine and the amount of Egypt’s imports from Ukraine and Russia together.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".