Conservation-Targeted Hydrologic-Economic Models for Water Demand Management
Bibliographic record
Abstract
Two basin-wide hydrologic-economic optimization models are presented to estimate how much water can be conserved while maintaining at least the same level of economic output. Water consumption is interpreted as either water diverted to consumptive users or water consumed by all users. Two different formulations for representing the two interpretations of water consumption are examined. The characteristics of different users, such as the consumption ratio and productivity, are considered. The models are applied to the South Saskatchewan River Basin (SSRB) in southern Alberta, Canada, where water scarcity is a severe issue. It is found that: a substantial amount of water can be conserved without sacrificing economic output; irrigation is the largest contributor while municipal and industrial (MI) users make a small difference in terms of water conservation; MI users make major economic contribution in order to retain the same level of system-wide aggregated benefits, and thereby overall water productivity can be considerably improved; MI users’ reactions are diversified depending on the specified conservation targets; and overall water conservation may be limited if MI users act independently. The implications of the results can be used to facilitate a better understanding of present water usage and guide policy makers into making informed decision for water demand management.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".