Enhancing Watershed Management Through the Characterization of the River Restoration Index (RRI): A Case Study of the Samian Watershed, Ardabil Province, Iran
Why this work is in the frame
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Bibliographic record
Abstract
The mountainous Samian Watershed hosts important rivers recently, significantly triggered by fast and unplanned urbanization, population growth, environmentally hazardous industrialization, and inappropriate dam construction. Nonetheless, this watershed has not yet been evaluated through the lens of river restoration. Therefore, this study aims (1) to apply the River Restoration Index (RRI), (2) to assess the significance of each river restoration criterion and sub-index, and (3) to identify priority hotspots for immediate restoration efforts across 27 sub-watersheds in this case study. First, we built a database containing meteorological, hydrological, land use, physiographic, soil, and economic data. Then, we calculated the general state of the watershed (GSW), connectivity (Con), riverbank conditions (RbC), and hydraulic risk reduction (HRR) sub-indices to develop a multi-domain RRI. Finally, the MEREC-ORESTE hybrid method supported sustainable government planning. The findings reveal significant environmental issues, notably in sanitation conditions, transversal connectivity, and urban encroachment on riverbanks. Sanitation risks were high throughout the watershed, while other eco-environmental risks varied across regions. The weights of 0.36, 0.16, 0.32, and 0.16 were assigned for GSW, Con, RbC, and HRR, respectively, highlighting the importance of GSW and RbC in river restoration activities. Priority management areas (with RRI below 0.50) cover 78% of the watershed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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