Integrated geophysical investigations of groundwater for sustainable management in Faisalabad region of Pakistan
Bibliographic record
Abstract
Abstract As global and local populations surge and climate change continue to disrupt surface and groundwater reservoirs, the urgent need arises to explore additional groundwater sources. Ensuring sustainable management necessitates the efficient identification of high-potential zones to meet escalating water demands. This study aims to delineate groundwater potential zones in Faisalabad District, Pakistan, utilizing a cost-effective geoelectrical resistivity survey method. Apparent resistivity data was collected using the Schlumberger electrode configuration and analyzed with the Interpex “IX1D v2 model” to determine true soil layer resistivities and thicknesses with average root mean square error of 5.12%. The results have revealed that the Aquifer thickness ranged from 13.35 to 165.59 m, and resistivity from 23.96 to 1125.0 Ωm. Hydraulic conductivity, transmissivity, and porosity of aquifers varied from 0.49 to 24.11 m/day (average 8.214 m/day), 35.67 to 1593.98 m2/day (average 567.771 m2/day), and 22.29 to 39.82% (average 37.465%), respectively. Integration of resistivity and geo-hydraulic properties data identified vertical electrical sounding (VES) points 1, 3, 4, and 6 as highly suitable for large-scale freshwater extraction due to having high groundwater potential repositories (coarse sand and gravel formations). Other points had varying suitability: VES points 7 and 8 for shallow wells only, VES points 5, 9, and 10 not recommended due to hard formations, and VES point 2 due to poor groundwater quality. This integrated approach has proven effective in assessing groundwater strata to support Sustainable Development Goal (SDG-3), making it applicable to other geographic locations and informing policy decisions for effective groundwater management.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".