The Aral Sea Basin Water for Sustainable Development in Central Asia
Bibliographic record
Abstract
This book offers the first multidisciplinary overview of water resources issues and management in the Aral Sea Basin, covering both the Amu Darya and Syr Darya River Basins. The two main rivers of Amu Darya and Syr Darya and their tributaries comprise the Aral Sea Basin area and are the lifeline for about 70 million inhabitants in Central Asia. Written by regional and international experts, this book critically examines the current state, trends and future of water resources management and development in this major part of the Central Asia region. It brings together insights on the history of water management in the region, surface and groundwater assessment, issues of transboundary water management and environmental degradation and restoration, and an overview of the importance of water for the key economic sectors and overall socio-economic development of Central Asian countries, as well as of hydro politics in the region. The book also focusses on the future of water sector development in the Basin, including a review of local and international actors, as well as an analysis of the current status and progress towards the Sustainable Development Goals by Basin countries. The book will be essential reading for those interested in sea basin management, environmental policy in Central Asia and water resource management more widely. It will also act as a reference source for decision-makers in state agencies, as well as a background source of information for NGOs
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".