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
Historically, water scarcity was a local issue. It was up to national governments to balance water supply and demand. Now this is changing as scarcity crosses national boundaries via the international grain trade. Since it takes 1,000 tons of water to produce one ton of grain, importing grain is the most efficient way to import water. Countries are, in effect, using grain to balance their water books. Similarly, trading in grain futures is in a sense trading in water futures. Falling water tables are already adversely affecting harvests in some countries, including China, the world's largest grain producer. Overpumping has largely depleted the shallow aquifer under the North China Plain, forcing farmers to turn to the region's deep fossil aquifer, which is not replenishable. Wheat farmers in some areas of the Plain are now pumping from a depth of 300 meters, or nearly 1,000 feet. Overall, China's grain production has fallen from its historical peak of 392 million tons in 1998 to an estimated 358 million tons in 2005. This drop of 34 million tons exceeds the Canadian wheat harvest. China largely covered the drop-off in production by drawing down its once vast stocks until 2004, at which point it imported 7 million tons of grain. Water shortages are even more serious in India simply because the margin between actual food consumption and survival is so precarious. At this point, the harvests of wheat and rice, India's principal food grains, are still increasing. But within the next few years, the loss of irrigation water could override technological progress and start shrinking the harvest in some parts of the country, as it is already doing in China. After China and India, there is a second tier of countries with large water deficits -- Algeria, Egypt, Iran, Mexico, and Pakistan. Three of these -- Algeria, Egypt, and Mexico -- already import much of their grain. However, in a parallel move with China, water-short Pakistan abruptly turned to the world market in 2004 for imports of 1.5 million tons of wheat. Its need for imports is likely to climb in the years ahead. The Middle East and North Africa -- from Morocco in the west through Iran in the east -- has become the world's fastest-growing grain import market. The demand for grain is driven both by rapid population growth and by rising affluence, much of the latter derived from the export of oil. With virtually every country in the region pressing against its water limits, the growing urban demand for water can be satisfied only by taking irrigation water from agriculture. Egypt, with some 74 million people, has become a major importer of wheat in recent years, vying with Japan -- traditionally the leading wheat importer -- for the top spot. It now imports 40 percent of its total grain supply, a number that edges steadily upward as its population outgrows the grain harvest produced with the Nile's water.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.028 |
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