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Record W4403364348 · doi:10.1016/j.jappgeo.2024.105542

Insights from electrical resistivity tomography on the hydrogeological interaction between sand dams and the weathered basement aquifer

2024· article· en· W4403364348 on OpenAlexaff
Hannah Ritchie, Ian Holman, Justus Nyangoka, Paul Bauman, Alison Parker

Bibliographic record

VenueJournal of Applied Geophysics · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsBGC Engineering (Canada)
FundersEngineering and Physical Sciences Research Council
KeywordsElectrical resistivity tomographyGeologyHydrogeologyAquiferElectrical resistivity and conductivityBasementGeotechnical engineeringTomographyGeomorphologyPetrologyMining engineeringGeochemistryGroundwaterCivil engineeringEngineering

Abstract

fetched live from OpenAlex

Sand dams, composed of recent alluvial aquifers behind concrete dam walls, are a water management technique in drylands. However, their level of hydraulic connectivity with their surrounding weathered basement aquifer is debated. This study aims to constrain this hydrogeological uncertainty in order to better understand their ability to meet water needs and improve dryland water security. The study is the first to use 2D geophysics (Electrical Resistivity Tomography) to provide evidence of seepage from sand dams at three mature and three newly built sites. A generally greater hydraulic connectivity was found between sand dams and their surrounding aquifer than has been assumed in some previous studies, with sites providing at least some local recharge rather than existing as isolated storage structures. This improved understanding is beneficial for both site selection and the performance of sand dams and can help ensure that maximum benefits are derived from the construction of a sand dam depending on its intended purpose. • Electrical Resistivity Tomography successfully used for the first time at sand dams. • The nature of the weathered basement allows seepage from sand dams. • Sand dams exists predominantly as managed aquifer recharge structures.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.944
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.238
Teacher spread0.222 · 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 designOther design
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

Citations6
Published2024
Admission routes1
Has abstractyes

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