Water Shortages Threaten Food Future in the Arab Middle East
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
Long after the political uprisings in the Middle East have subsided, many underlying challenges that are not now in the news will remain. Prominent among these are rapid population growth, spreading water shortages, and ever growing food insecurity. In some countries, grain production is now falling as aquifers are depleted. After the Arab oil-export embargo of the 1970s, the Saudis realized that since they were heavily dependent on imported grain, they were vulnerable to a grain counter-embargo. Using oil-drilling technology, they tapped into an aquifer far below the desert to produce irrigated wheat. In a matter of years, Saudi Arabia was self-sufficient in wheat, its principal food staple. But after more than 20 years of wheat self-sufficiency, the Saudis announced in January 2008 that this aquifer was largely depleted and they would be phasing out wheat production. Between 2007 and 2010, the wheat harvest of nearly 3 million tons dropped by more than two thirds. At this rate the Saudis likely will harvest their last wheat crop in 2012 and then be totally dependent on imported grain to feed their Canada-sized population of nearly 30 million people.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.011 |
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