Bibliographic record
Abstract
Life on earth depends on water. Unfortunately, water resources are not evenly distributed. There are countries with abundant water supplies, such as Brazil or Canada, and countries that lack water resources, such as Egypt or Jordan. Because water is critical for the production of food and other goods, as well as for human consumption, recreation and ecosystem support, competition among the various users for available supplies is often intense. The problem is compounded by the fact that water markets often work imperfectly or are lacking altogether. What can countries with limited water resources do? In rare cases, it may be possible to transfer water from water-abundant regions. For example, the small African country of Lesotho has abundant water supplies and sells its surpluses to South Africa (Mwangi, 2007). Another possibility is to build hydraulic infrastructures (wells, desalination plants, dams, etc.), which can be very expensive and often prove to be environmentally problematic (Velazquez, 2007). Yet another possibility is to consider importing agricultural products that require a lot of water during their production processes. Imports of such goods reduce the need to produce them in the country with scarce water resources. Water imported in the form of water-intensive goods is often referred to as “virtual 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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.278 | 0.072 |
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".