Bibliographic record
Abstract
Abstract This entry examines what is known regarding the economic characteristics of residential water use and how this type of information can be applied in managing water resources. Understanding the economic dimension of household water use is important for several reasons. First and most importantly, all households on the planet share the need for access to potable water supplies. On the other hand, households have very different degrees of access to safe water. For most of North America and Europe, access to clean water is almost taken for granted by many households. In contrast, a great number of households in low‐income countries do not have access to reliable supplies of potable water. As the World Bank contends, “The challenge is enormous: one billion people still lack access to safe water, two billion lack safe sanitation. Slow progress is not acceptable, as more than three million children still die every year from avoidable water‐related disease” (World Bank Water Supply and Sanitation web‐site, September 1, 2001 http://www.worldbank.org/html/fpd/water/ ). Second, the agricultural sector dominates global water use, but residential and industrial demands have grown much more rapidly during the twentieth century. Third, empirical evidence indicates that the amount of water used by a household is a complex function of a number of influences, including the price of water (both in monetary terms and in time costs), household income, and household characteristics (for example, the number of residents and water‐using appliances).
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".