Analysis and assessment of water resources in the Kyrgyz Republic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The study aimed to analyse the factors of water use, environmental impacts and efficiency of water management to develop recommendations for their optimisation. The study determined that water consumption in agriculture decreased from 80% in 2020 to 76% in 2024, but water losses in irrigation systems remained high, decreasing only from 39% to 38%. In the Chui region, the largest water consumer, the share of water use decreased from 45% to 41%, and economic losses reduced from USD 40 million to USD 35 million. In water-scarce Osh region, water consumption dropped from 18% to 14%, but water availability in agriculture and the municipal sector remained limited. Wastewater treatment improved from 50% in 2020 to 55% in 2024, but this figure was far below international standards, where Switzerland and Canada had treatment rates of 95% and 90%, respectively. Comparative analysis demonstrated that developed countries are actively using digital leakage monitoring systems, smart irrigation technologies and multi-stage water treatment, which have reduced losses by up to 6-8%. In Kyrgyzstan, such technologies were introduced locally and only in some agricultural enterprises. Investments in water infrastructure amounted to USD 7 per capita, compared to USD 200 in Switzerland and USD 150 in Canada, which limited the modernisation of the water supply system. The problems identified confirmed the need to reform the water management system, including reducing water losses, modernising wastewater treatment facilities, introducing digital solutions for water management and adapting infrastructure to changing climate conditions
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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