Water Sustainability Index: Application of CWSI for Ahwaz County
Bibliographic record
Abstract
Sustainability of water resources is vital especially for developing countries such as Iran which are located in the Middle East and North Africa (MENA) region where water is scarce. To balance the high demand of water for economical growth and at the same time preserve the environment for present and future generations, sustainability of water resources should be considered by monitoring and data mining. For this purpose, several quantified indices have been proposed and applied world wide recently. In this paper, the Canadian Water Sustainability Index (CWSI) proposed by PRI, has been trailed for the case of Ahwaz County, a community located in South West of Iran fed by Karun River. Required data for the composite CWSI score which is the average of five major theme-based components (i.e. resource, ecosystem health, infrastructure, human health capacity) was collected according to the PRI evaluation method. In addition to the standardized CWSI, the final index was also calculated considering weight estimation for the five components by pair-wise comparison, using Expert Choice version 2000. Results showed that application of this index as a policy tool, with some modifications in weights, was satisfactory for the educational case study and could be replicated for other communities 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".