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Record W2582782602 · doi:10.1520/gtj20150286

Compacted Clay: Difficulties Obtaining Good Laboratory Permeability Tests

2017· article· en· W2582782602 on OpenAlexaff
Raphaële Chapuis

Bibliographic record

VenueGeotechnical Testing Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsHydraulic conductivityCompactionPermeability (electromagnetism)PorosityGeotechnical engineeringPermeameterSaturation (graph theory)Degree of saturationSoil scienceProctor compaction testMaterials scienceGeologyMineralogyMathematicsChemistrySoil water

Abstract

fetched live from OpenAlex

Abstract Testing the permeability of compacted clay is not easy. The hydraulic conductivity, k(Sr), depends upon porosity nc and degree of saturation Src after compaction, and the values reached during the permeability test, ncf and Srf. The four values are needed to predict k(Sr) using a dual-porosity model with two parameters, a and b. However, many published test reports do not give these four values. The tested clay is often unsaturated, and the measured k(Sr < 100 %) may be confused with its saturated value, ksat, whereas it may be one to three orders of magnitude lower than ksat. This, in turn, may lead a designer to poorly predict the total leakage of a lined cell or lagoon. For fully documented test data, parameter a is between 0.001 and 0.1 and parameter b is between 2.7 and 3.3 (around 3 for a perfect cubic law). Once the values of a and b have been found with correctly performed and fully documented tests, a local k(Sr) value can be predicted at each place the field density and Src have been assessed. This yields many predicted local k(Sr) values, which can then be used with statistics to predict the full- or large-scale hydraulic conductivity and leakage of a liner or cover.

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)0.004
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 score0.604
Threshold uncertainty score0.871

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.033
GPT teacher head0.261
Teacher spread0.228 · 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

Citations9
Published2017
Admission routes1
Has abstractyes

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