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Record W2988964513 · doi:10.1139/cgj-2018-0301

Impact of initial structural heterogeneity on long-term swelling behavior of MX80 bentonite pellet/powder mixtures

2019· article· en· W2988964513 on OpenAlexvenueno aff
Agustín Molinero Guerra, Nadia Mokni, Yu-Jun Cui, Pierre Delage, Anh Minh Tang, Patrick Aimedieu, Frédéric Bernier, Michel Bornert

Bibliographic record

VenueCanadian Geotechnical Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
Fundersnot available
KeywordsBentoniteSwellingPelletMaterials scienceWettingComposite materialHomogeneousRelative humidityInfiltration (HVAC)Chemical engineeringThermodynamics

Abstract

fetched live from OpenAlex

To better understand results of SEALEX in situ tests carried out at Tournemire Underground Research Laboratory, the hydromechanical behavior of a pellet/powder MX80 bentonite mixtures prepared at a dry density of 1.49 Mg/m 3 were investigated by means of microfocus X-ray computed tomography (μ-CT) observations and laboratory small scale infiltration tests. Radial and axial swelling pressures as well as relative humidity were monitored while wetting. Two configurations were considered: for the first, a pellet/powder mixture was prepared following a specific protocol to minimize initial structural heterogeneity; the second one was specially designed to study a strong heterogeneous mixture distribution. μ-CT observations performed on the two samples during hydration revealed an apparently homogeneous sample for the first mixture after 100 days of hydration. For the second specimen, several voids were still observed after 40 days of hydration. A comparison was made between the in situ and mock-up tests. It was observed that the evolutions of radial and axial swelling pressures depend on the initial heterogeneous distribution of the mixture. This heterogeneity is due to the different dry density values at the vicinity of the different sensors. The final values of axial swelling pressures were different for both configurations for the same global dry density.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.628
Threshold uncertainty score0.756

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.274
Teacher spread0.261 · 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

Citations21
Published2019
Admission routes1
Has abstractyes

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