Assessing artificial groundwater recharge on irrigated land using the MODFLOW model
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
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.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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