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Record W4281886913 · doi:10.18280/i2m.210206

An Application of Multi-Frequency Alternating Current Source for VES on Soil Resistivity Measurement and Investigation

2022· article· en· W4281886913 on OpenAlexvenueno aff
Pratimakorn Hakaew, Piyapat Panmuang, Prakasit Prabpal, Chonlatee Photong

Bibliographic record

VenueInstrumentation Mesure Métrologie · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsnot available
FundersMahasarakham University
KeywordsElectrical resistivity and conductivitySoil resistivityCurrent (fluid)HomogeneousMaterials scienceDepth soundingSoil testSoil scienceAlternating currentAcousticsField (mathematics)Soil waterGeotechnical engineeringEnvironmental scienceGeologyElectrical engineeringPhysicsEngineeringVoltageMathematics

Abstract

fetched live from OpenAlex

This paper is present an application of multi-frequency alternating current source that can be adjust the frequency from 1 Hz to 1 kHz for vertical electrical sounding (VES) on soil resistivity investigations. Researchers have used the four-point electrodes array method for resistivity method in laboratory and field trial of soil resistivity measurements. The result in laboratory found that in each frequency of current source has significate influence on the homogeneous soil resistivity. It was shown that the implemented equipment can be used to measure the soil resistivity as required. In field trial the soil resistivity was investigated by the implemented equipment feeding the rectangular wave current through two current electrodes on the subsurface soil which embedded in the non-homogeneous soil in the field work. The current source can be scaled and frequency adjusted at 50 Hz, 100 Hz, 200 Hz, 500 Hz and 1 kHz, respectively. The subsurface field have repeated tests 30 times in each frequency by compared with standard resistivity measurement equipment. The result found that the non-homogeneous apparent soil resistivity can be investigated and at the frequency of 100 Hz is close to the standard tool.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.877
Threshold uncertainty score0.353

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.072
GPT teacher head0.316
Teacher spread0.244 · 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 designObservational
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

Citations1
Published2022
Admission routes1
Has abstractyes

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