Sustainability Assessment: The role of Indicator-based Frameworks in Sustainable Water Management
Bibliographic record
Abstract
The concept of sustainability in water management remains an amorphous notion. Despite its widespread use, there is not a shared and accepted definition of the concept of sustainability in water management, including its monitoring and assessment, particularly at river basin scale. Sustainability Assessment (SA) can be defined as any process that aims at planning and direct decision-making toward sustainable development. An interdisciplinary approach for understanding, measuring and monitoring sustainability of water management practices includes the holistic development of Indicator-Based Assessment (IBA) frameworks as policy/decision support tools. The IBA refers to the positive, negative, and neutral qualifications of an indicator based on the comparison between its observed evolution (and/or status), and the desired evolution set for the indicator by means of a frame of reference. Therefore, developing IBA frameworks help to synthesize information and monitor changes in water management systems. Recently, developing indicator-based assessment frameworks and constructing indexes have evolved significantly toward monitoring the United Nations Sustainable Development Goals (UN SDGs). However, there are limited studies on developing sustainability indexes or indicator-based sustainability assessment frameworks at the river basin scale for complex issues of water management. The aim of this work is to provide a review of i) the concept of SA in water management and also ii) the methodology of indicator-based framework development. Finally, a case study of developing an indicator-based sustainability assessment framework is presented for the Mashhad River basin in Iran.
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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 | 0.041 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".