Regional Multivariate Indices of Water Use Potential for the Continental United States
Bibliographic record
Abstract
The necessity of freshwater for sustaining human life has prompted the development of numerous estimation techniques and metrics for understanding where, when, and why water is used. While estimates are valuable, techniques for estimating water use vary, and may be difficult to replicate and/or unavailable on an annual basis or at the regional scale. To address these drawbacks, this paper proposes a series of regional indices for the continental United States that could serve as proxies for water use that are based on key variables associated with water use. Regional indices at the county level are computed, compared against each other, and compared to water withdrawal estimates from the United States Geological Survey (USGS). These comparisons highlight differences amongst the derived indices and the water withdrawal estimates. They also demonstrate promise for future development and implementation of related indices, given their similarities with water withdrawal estimates. Using only a small set of variables, these indices achieve some degree of similarity (~20%) to estimates of water withdrawals. The comparative data availability and ease of estimating these indices, as well as the ability to decompose the additive indices into their constituent use categories and constituent variables, renders them practically useful to water managers and other decision makers for identification of locally specific drivers of water use and implementation of more geographically-appropriate policies to manage scarce water resources.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".