Bibliographic record
Abstract
The price of water is increasing -- sometimes dramatically -- throughout the world. Over the past five years, municipal water rates have increased by an average of 27 percent in the United States, 32 percent in the United Kingdom, 45 percent in Australia, 50 percent in South Africa, and 58 percent in Canada. In Tunisia, the price of irrigation water increased fourfold over a decade. A recent survey of 14 countries indicates that average municipal water prices range from 66-cents per cubic meter in the United States up to $2.25 in Denmark and Germany. Yet consumers rarely pay the actual cost of water. In fact, many governments practically (and sometimes literally) give water away for nothing. The average American household consumes about 480 cubic meters (127,400 gallons) of water during a year. Homeowners in Washington, DC, pay about $350 (72-cents per cubic meter) for that amount. Buying that same amount of water from a vendor in the slums of Guatemala City would cost more than $1,700. The price people pay for water is largely determined by three factors: the cost of transport from its source to the user, total demand for the water, and price subsidies. Treatment to remove contaminants also can add to the cost. The cost of transporting water is determined largely by how far it has to be carried and how high it has to be lifted. Growing cities and towns may have to go hundreds of kilometers to find the water needed to satisfy their increasing thirst. California cities have long imported water from hundreds of kilometers away. And China is constructing three canals that are 1,156 kilometers, 1,267 kilometers, and 260 kilometers long to transfer water from the Yangtze River to Beijing and other rapidly growing areas in the northern provinces. Pumping water out of the ground or over land to higher elevations is energy-intensive. Pumping 480 cubic meters of water a height of 100 meters requires some 200 kilowatt-hours of electricity. At a price of 10-cents per kilowatt-hour, the cost is $20 -- not including the cost of the pump, the well, and the piping. One hundred meters is not an unusual lift for wells tapping falling supplies of groundwater. In Beijing and other areas in northern China, for instance, lifts of 1,000 meters are sometimes required. Mexico City, at an elevation of 2,239 meters, has to pump some of its water supply over 1,000 meters up a mountain. The operating costs alone amount to $128.5 million annually. Pumping this water requires more energy than is consumed overall in the nearby city of Puebla, home to 8.3 million people. Amman, Jordan, faces a similar problem related to delivering water to higher elevations.
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 | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.167 | 0.056 |
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".