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Record W2775812048 · doi:10.1144/qjegh2016-136

Glacio-marine clay resistivity as a proxy for remoulded shear strength: correlations and limitations

2017· article· en· W2775812048 on OpenAlexaboutno aff
Michael Long, A.A. Pfaffhuber, Sara Bazin, Kristoffer Kåsin, Anders Samstad Gylland, Alberto Montaflia

Bibliographic record

VenueQuarterly Journal of Engineering Geology and Hydrogeology · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyGeotechnical engineeringElectrical resistivity and conductivityShear (geology)Shear strength (soil)GeomorphologyPetrologySoil scienceSoil water

Abstract

fetched live from OpenAlex

In geotechnical engineering in Norway, Sweden and Canada the presence of sensitive and/or quick clays poses a major challenge. Formation of these clays involves the leaching of salt from the pore fluid. Thus it has been recognized that electrical resistivity measurements could be useful in delineating leached and unleached clays. This paper seeks to assess the applicability, repeatability and reliability of the various geophysical techniques in the study of sensitive clays. It also attempts to understand the factors that control the measured resistivity and in particular to determine the limitations of directly obtaining the remoulded shear strength from the resistivity measurements. It was found that borehole, surface and airborne resistivity measurements are accurate and compatible. For the 30 Norwegian sites studied it was found that resistivity is primarily defined by the porewater salt content, with minor additional influence by clay content and plasticity, and porosity. A relationship exists between resistivity and remoulded shear strength but this is limited to material deeper than the dry crust and a surface weathering zone of about 7.5 m thickness. High resistivity (>10 Ω m) may indicate quick or weathered clay but low resistivity (<10 Ω m) conclusively points to stable, unleached clay.

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)0.001
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 score0.703
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.016
GPT teacher head0.246
Teacher spread0.230 · 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

Citations10
Published2017
Admission routes1
Has abstractyes

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