Why the World’s Food Basket Became the Largest Grains’ Importer Country? “Comparative Statement on Main Crops’ Self-Sufficiency in Egypt and in China”
Bibliographic record
Abstract
<p>How a country as Egypt which is formerly known as the world’s food basket and the gift of the Nile River became the largest grains’ importer country of wheat? Why agricultural field in Egypt could not produce enough food for its people? And how does China succeeded to depend on itself to be able to feed its huge population? The current study used 4 crops (wheat, maize, rice and soybean), with three indexes (production, import, and domestic supply quantity) chosen to measure self-sufficiency (Ss). The study found out that, Egypt has a negative self-sufficiency for wheat, maize and soybean. Agricultural policies are the key of China’s successful development and at the same time are the main factors which affected agriculture in Egypt and make it fail to produce enough food. This study highly recommends policy makers in Egypt to make an improvement on agricultural policies for the purpose of promoting the agricultural self-sufficiency by supporting farmers’ to produce enough food.</p>
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".