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Record W3037266671 · doi:10.1680/jenes.19.00056

A Novel Electrode Array for Electrical Resistivity Tomography to Assess Groundwater Resources: Field Test at Liwa UAE

2020· article· en· W3037266671 on OpenAlexvenueno aff
Saif Ullah, Bing Zhou, Muhammad Asim Iqbal

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsnot available
Fundersnot available
KeywordsElectrical resistivity tomographyAquiferElectrode arrayGeologyTomographyGroundwaterElectrodeInversion (geology)Electrical resistivity and conductivityRemote sensingGeotechnical engineeringOpticsGeomorphologyElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score0.353

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.206
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2020
Admission routes1
Has abstractyes

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