Understanding contemporary challenges for water security in Ulaanbaatar, a semi-arid region in Mongolia
Bibliographic record
Abstract
Water security is one of the biggest challenges of the 21stcentury. Understanding context-specific challenges and opportunities around this issue is key to improving water systems globally. This paper explores the current state of urban water security in Ulaanbaatar, Mongolia’s capital city. Ulaanbaatar is home to more than 40% of the country’s population and 60% of its national GDP. The city is located in the Tuul River basin and relies almost entirely on groundwater aquifers of the Tuul River for its supply of clean drinking water. In recent years, socio-economic stressors resulting from rapid urbanisation and environmental pressures have intensified the levels of degradation of the Tuul River and intensified the risks of water insecurity for the population of Ulaanbaatar. First, this paper draws from an extensive literature review and document analysis to provide an overview of the current state of urban water security in Ulaanbaatar. Secondly, the discussion is substantiated with information from key informant interviews which aim to explain the ongoing challenges for water security in Ulaanbaatar and suggest paths for improvement. This study finds that the main challenges for water security in Ulaanbaatar are data unavailability, limited human and financial resources across public water sector agencies, exacerbating flood risk and ongoing water quality disparities between the central city area and peri-urban ger districts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".