Assessing artificial groundwater recharge on irrigated land using the MODFLOW model
Bibliographic record
Abstract
Water-resource deficits have led to the need for artificial groundwater-recharge techniques to provide drinking water for rural communities in southeastern Kazakhstan, especially those with a small number of inhabitants. The Kishi-Tobe settlement located in the Karatal agricultural area on the right bank of the Karatal River in southeastern Kazakhstan has severe water-supply shortages. In this study, the groundwater-flow model MODFLOW was used to simulate complex hydrogeological and irrigation conditions for a quantitative assessment of artificial groundwater recharge from infiltration pools. The aim of these pools was to solve the water shortage in the Kishi-Tobe settlement. New findings showed that the maximum rate of artificial groundwater recharge from the infiltration pool can reach 1000 m3 day?1, corresponding to an infiltration rate of 0.2 m day?1, which creates a groundwater mound with a radius of around 500 m from the center of the pool. The groundwater mound also serves as a hydrodynamic barrier, preventing inflow of contaminated groundwater from irrigated fields and rice checks to the pumping wells. The potential rate of groundwater pumping from two water-supply wells can reach up to 7350 m3 day?1 over 10 years, providing a maximum drawdown in the wells of about 24 m. The water required by the Kishi-Tobe settlement can be supplied at a rate of 864 m3 day?1, achieving both available drawdowns by the end of the forecast period and balanced provision of the groundwater resource.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".