Analysis and assessment of water resources in the Kyrgyz Republic
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
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".