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Record W2468754075 · doi:10.1680/jgeot.15.p.217

Effects of tube sampling in soft clay: a microstructural insight

2016· article· en· W2468754075 on OpenAlexaboutno aff
Jubert Pineda, Xianfeng Liu, Scott W. Sloan

Bibliographic record

VenueGéotechnique · 2016
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
Fundersnot available
KeywordsMercury intrusion porosimetryVoid ratioTube (container)Geotechnical engineeringSampling (signal processing)Materials sciencePorosityPorosimetryPiston (optics)GeologyVoid (composites)MineralogyComposite materialPorous mediumEngineering

Abstract

fetched live from OpenAlex

This paper analyses the effects of tube sampling in soft clay with particular emphasis on the modifications that occur in the clay fabric for tube specimens retrieved using open samplers (Shelby) as well as a fixed-piston sampler. Tube specimens of an estuarine soft clay retrieved from the National Soft Soil Testing Facility located at Ballina in northern New South Wales (Australia) are analysed in this study. Mercury intrusion porosimetry tests are carried out to infer the pore size density function of specimens trimmed at different locations along the tube and these are compared against those performed on undisturbed clay, obtained from Sherbrooke specimens, to estimate variations in the natural soil fabric due to tube sampling. It is shown that tube sampling induces important modifications in the natural pore size distribution (PSD) of the clay, not only at the perimeter but also at the centreline of the sampler. This phenomenon is more pronounced at the top and bottom ends of the sampler and increases with a decrease in the sampler diameter. The use of the large-diameter fixed-piston sampler reduces dramatically the variations in both the micro and macro void ratios during tube sampling.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.338

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 head0.006
GPT teacher head0.204
Teacher spread0.198 · 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 designBench or experimental
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

Citations29
Published2016
Admission routes1
Has abstractyes

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