A Novel Electrode Array for Electrical Resistivity Tomography to Assess Groundwater Resources: Field Test at Liwa UAE
Bibliographic record
Abstract
A novel electrode configuration, called enhanced gradient (EGD) array, for two-dimensional (2D) electrical resistivity tomography (ERT) is introduced. The sensitivity, resolution and penetration depth of some popular traditional electrode arrays are compared with those of the new proposed array. Three synthetic geological models – namely, blocks, faults and fold – were used to check the efficiency of the new electrical resistivity survey for subsurface imaging. The proposed EGD array with two most popular arrays (Schlumberger and gradient) was employed to image the artificial aquifer in Liwa area, Abu Dhabi, UAE. The 2D ERT inversion results showed that the new electrode array yields much better images than the Schlumberger and gradient arrays for imaging the synthetic models and real artificial aquifer in the Liwa area of UAE. The new EGD array has advantages over the popular gradient array in terms of pseudo-section coverage and imaging resolution. The novel EGD array can be employed for 2D ERT subsurface mapping application, particularly in groundwater imaging and monitoring surveys.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".