Drainage in the Aral Sea Basin
Bibliographic record
Abstract
The intensity of irrigation in Central Asia requires artificial drainage in order to control waterlogging and salinization. There are about 5.35 million ha with a combination of surface drainage, and vertical and horizontal subsurface drainage. Of the five Central Asian republics, Uzbekistan is the country with the most significant artificially drained land, of approximately 1 million ha. There have been several innovations in drainage design in the region, in order to account for seepage from irrigation canals and upstream irrigated lands, percolation from excess irrigation water, groundwater fluxes to the root zone, and the accompanying salts moving into the crop root zone. Deeper subsurface drainage depths are considered essential for the control of waterlogging and salinity. There were significant investments in drainage in the region until the 1990s. However, with the collapse of the Soviet Union and the deterioration of economic conditions in Central Asia, investment in drainage declined. Drainage systems are no longer properly maintained and the areas suffering from salinization and waterlogging have been increasing. The drainage problems are compounded by the weakened institutional structure to successfully operate and maintain the drainage network. This paper addresses the technical and institutional improvements required to improve drainage performance, and stresses the importance of implementation of drainage with irrigation in the context of integrated water resources management. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".