Economic Aspects of Irrigation Water Pricing
Why this work is in the frame
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Bibliographic record
Abstract
Worldwide, irrigation water consumes the bulk of renewable fresh water resources. As water demand increases with rising living standards and population growth, and as prospects for water diversion (extraction) are limited in some regions and nonexistent in others, the course of water policy left open is to increase efficiency of water use. This requires taking account of the full cost of water and the way to achieve this goal inevitably leads to some form of water pricing. Yet, water policy makers and economists are far from agreeing on what constitutes the "right" price of water in any given circumstance and how this price is to be charged. This paper aims to clarify and reconcile some of the conflicting views by discussing the economic aspects underlying irrigation water pricing and their implementation in practice.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it