Enhancing water sustainability in the Gobi Desert: processes based on IWRM principles
Bibliographic record
Abstract
Abstract The mining industry is an important sector that contributes to economic growth and employment creation in Mongolia. Water access, water quality, and community engagement are the major challenges the Mongolian mining industry faces. Integrated Water Resource Management (IWRM) is a holistic water management approach that applies principles of economic efficiency, social equity, and environmental sustainability to ensure water sustainability. A research study was carried out to understand stakeholders’ views and perspectives on IWRM and to identify water use practices, challenges, and barriers in the Gobi Desert mining region. The aim was to identify processes that help to improve access to water in the Gobi Desert region. This research applied a qualitative approach and employed three data collection methods: (1) semi-structured interviews; (2) field observations and (3) documents and academic articles reviews. Research participants were representatives from mining companies, local communities, government, and river basin administrations. In the Gobi Desert region, processes contributing to improving water management are: (1) participatory water monitoring, (2) coal processing plant educational visits, (3) local stakeholders council’s meetings, (4) herder’s well improvement projects, (5) independent water auditing, and (6) water advocacy events. These practices, aligned with the core principles of IWRM provide practical solutions for sustainable water management in mining regions, with the potential for global adaptation.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".