Licensed Under Creative Commons Attribution CC BY The Factors Affecting on the World Prices of the Most Important Agricultural Commodities and their Effects on the Egyptian Domestic Prices
Bibliographic record
Abstract
Abstract: The most important results of the study are represented in that the world prices of wheat are affected by some factors. The most important of these factors are America’s export price of wheat, Russia’s export price of wheat and Canada’s production of ethanol. However, the most important factors affecting on corn crop are represented in the amount of the global exports of the crop, America’s export price of the crop, France’s export crop of the crop and France’s production of ethanol. Finally, for soybeans, the most important factors are represented in America’s export price, Brazil’s export price and the amount of the global exports of soybeans. The study found, also, that the rise in the world prices of the three crops leads to an increase in their domestic prices and a rise in their import prices. Finally, it is shown that the rise in the import prices of these commodities leads to a rise in the prices of the local production and a rise in the Egyptian import bill. Thus, the most important recommendations of the study are confined in two trends. The first trends are represented in the vertical expansion through the produce high productive varieties and circulate them through activating the role of the agricultural guides. However, the second trend is represented in the horizontal expansion which is faced by a lot of obstacles foremost the problem of water scarcity in Egypt. Here, the state shall adopt two trends whether to activate the Egyptian-Sudanese relation and exploit the Egyptian expertise in cultivating the untapped areas in Sudan or reconsider, seriously, Congo River project which may results in providing about 95 billion cubic meters of water annually for Egypt. This amount of water can be exploited in reclaiming the lands suitable for reclamation and exploiting them in agriculture and thus the problem of the Renaissance Dam can be
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.576 | 0.441 |
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".