Bibliographic record
Abstract
Abstract The management of water resources systems involves influencing and improving the interaction among three subsystems: natural (biophysical), economic, and legal-institutional frameworks. In this sense, hydroeconomic models have the advantage of analyzing water management problems through models that explicitly represent these interactions. The combination of economic, engineering, and environmental aspects of management provides better-informed results for decision making in the complex environment in which water management operates. Hydroeconomic models (HEMs) are spatially distributed management models of a river basin or system in which both water supply and demands are economically and hydrologically characterized. This definition is sometimes relaxed to refer in general to water resources management models that include the economic component. In HEMs, the management and allocation of water is either driven by the economic value of water or economically assessed, which contributes to policy analysis and reveals opportunities for better economic management. The traditional view of water demand as a fixed requirement to be satisfied is modified by a view of demand that adapts to the changes in the scarcity of water. The integration of economics in HEMs allows the identification of the best combination of water supply and demand management options within a consistent framework. As water scarcity increases worldwide, water managers will increasingly turn to tools that reveal solutions to increase efficiency in water use, fostering improved economic development through better-informed policy choices.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.058 | 0.010 |
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".