Application of Geophysical Methods for the Delimitation of Potential Groundwater Zones in Rural Areas
Bibliographic record
Abstract
The search for groundwater has become a necessity because of the effects of climate change and the resulting water shortages.In rural areas, such as the "Pampa del Guasmo" (Yaguachi-Ecuador), freshwater is supplied by building shallow artisanal wells.Applying this practice sets challenges to complement the technical-scientific knowledge of aquifers, opening up the need to apply geophysical-hydrogeological techniques to characterize the terrain.The study aimed to identify areas with water saturation at greater depths using geoelectrical methods to determine suitable drilling sites that meet the community's water needs.The methodology includes: i) analysis of cartographic base information, ii) execution of electrical resistivity tomography (ERT) complemented with Multichannel Analysis of Surface Wave (MASW) + Microtremor Array Measurements (MAM) surface wave tests and rotary drilling, iii) preparation of geoelectrical profiles, correlation of geophysical-geoelectric and lithological information for the determination of potential groundwater zones.The results showed that the third layer with resistivities of 8.3-9.6 Ω.m in profile ERT-L1 and 10-15 Ω.m in profile ERT-L2 represent a semi-confined aquifer consisting of clayey-silty sand with the presence of gravel, located from 19 and 40 m depth, respectively.The application of geophysics is the key to identifying the potential for groundwater use, as corroborated by the drilling performed.Owing to the growing population in these sectors and the increased demand associated with lowering levels in excavated wells, the applied geophysics process verifies the need for deeper wells that must be managed concerning their use, environmental implications, and sustainability.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 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".