Development of a Watershed Sustainability Index for the Santiago River Basin, Mexico
Bibliographic record
Abstract
Sustainability indices are a way of quantifying the progress that a certain region has achieved in terms of sustainability that can be transmitted to society and decision makers. The watershed approach has become relevant for managing water resources and ensuring their sustainability. This study combined the above two approaches by applying an adapted watershed sustainability index (WSI) to evaluate the sustainable development of the Santiago–Guadalajara River basin (SGRB), which passes through Guadalajara, the second-most populous city in Mexico. The river is the most polluted waterway in the country. The WSI of each sub-basin places the SGRB at a sustainability level between low in the upper and lower basin region and intermediate in the central basin region. Regions with a low sustainability level are characterized by environmental degradation due to changes in land use, while in the region with intermediate sustainability, the factor that most affect the evaluation is water availability. An overall sustainability score of WSI = 0.36 was obtained for the study area, which is lower than that of any other basins evaluated in the same manner around the world. These results send a clear message to decision makers of the three government levels, in charge of the environmental sustainability of the basin, of the need to take action to facilitate its recovery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".