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Record W4412528752 · doi:10.1093/gji/ggaf278

Spontaneous potential surveys in surface fresh waters for engineering and environmental applications

2025· article· en· W4412528752 on OpenAlexafffund
S. L. Butler

Bibliographic record

VenueGeophysical Journal International · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGeologyEnvironmental scienceEarth scienceOceanography

Abstract

fetched live from OpenAlex

SUMMARY Geophysicists using the spontaneous potential method measure differences in electrical potential without providing an active source of current. Most spontaneous potential surveys have been carried out on land or in marine environments. In this paper, I evaluate the use of the spontaneous potential method in surface fresh water for small-scale environmental and engineering applications. In one survey reported here, the electrical potential between an electrode at the river edge and one suspended from a bridge was used to measure a high resolution profile across a river. In another, electrical potentials were measured between sets of electrodes mounted on a canoe. In both surveys, significant and consistent anomalies were detected particularly near bridge structures, and simple modelling in terms of point sources and line sources was undertaken to better characterize the causes of the anomalies. The possibility of an induction-induced voltage difference across the river caused by Earth’s magnetic field and flow in the river was also investigated. The absence of this potential is attributed to significant electrical conduction through the riverbed. This work demonstrates the utility of spontaneous potential as a technique for detecting and characterizing anomalies of environmental and engineering interest in fresh water environments.

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 score0.885
Threshold uncertainty score0.347

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 head0.005
GPT teacher head0.215
Teacher spread0.210 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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