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Record W2077055463 · doi:10.4133/jeeg8.4.263

Electrical Conductivity in Soils: Underlying Phenomena

2003· article· en· W2077055463 on OpenAlexaff
Katherine Klein, J. Carlos Santamarina

Bibliographic record

VenueJournal of Environmental and Engineering Geophysics · 2003
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsUniversity of Toronto
FundersNational Science Foundation
KeywordsThermal conductionSoil waterMaterials scienceMicroscale chemistryAnisotropyElectrical resistivity and conductivityPorosityConductivityIonic conductivitySurface conductivitySoil scienceMineralogyElectrolyteGeologyElectrodeChemistryComposite materialPhysicsOptics

Abstract

fetched live from OpenAlex

Electrical conductivity can be accurately and readily measured in the laboratory and in the field, with minimal electrode effects even in high specific surface soils and∕or high ionic concentration pore fluids. Electrical conductivity combines the contributions of particle conduction, surface conduction and pore fluid conduction, and the effects of particle shape and fabric. The interplay between participating soil parameters is often obscured in typical empirical equations, such as Archie’s law. New experimental results show that surface conduction is an important contributor to global soil conduction in high specific surface soils that are saturated with low-ionic concentration pore fluids; the relevance of surface conduction increases with decreasing porosity. On the other hand, pore fluid conduction prevails as the conductivity of the electrolyte and the porosity of the soil increase. Furthermore, low frequency conductivity anisotropy increases with increasing ionic concentration. Simple yet robust microscale analytical models properly capture the observed interplay between the most relevant soil parameters.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.003
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.015
GPT teacher head0.199
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), 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

Citations147
Published2003
Admission routes1
Has abstractyes

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