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Record W4388333494 · doi:10.3390/min13111412

Properties of Bentonite-Based Sealing Materials during Hydration

2023· article· en· W4388333494 on OpenAlexafffund
Mahsa Shafaei Bajestani, Othman Nasir, Won Taek Oh

Bibliographic record

VenueMinerals · 2023
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsUniversity of New Brunswick
FundersNew Brunswick Innovation Foundation
KeywordsBentoniteSwellingWettingCompactionGeotechnical engineeringMaterials scienceComposite materialGeology

Abstract

fetched live from OpenAlex

A typical deep geological repository (DGR) design consists of a multi-barrier system, including the natural host rock and the engineered barrier system. Understanding the swelling behavior of bentonite-based sealing materials (BBSM), as a candidate material for the engineered barrier system, is crucial for DGR’s long-term safety. In this study, a hydromechanical (HM) column-type test was designed to model the hydration of BBSM from the underground water and determine the resulting swelling pressure in vertical and radial directions. Five hydration tests were carried out on identical compacted samples of 70% bentonite and 30% sand (70-30 bentonite-sand) mixtures with a dry density of 1.65 g/cm3 for varied durations of hydration, between 1 day and 120 days. The experiments were performed parallel to the compaction direction. Following each HM column-type test, the advancement of the wetting front was determined for each test. After 120 days, 56,339 mm3 of water infiltrated the sample and the wetting front reached over 50% of the sample height. The evolution of axial swelling pressure revealed an initial increase in swelling pressure with time in all tests, followed by a reduction in the rate at later times. After early stages of swelling, radial sensors showed an increase in swelling pressure. After 120 days, the radial pressure sensor closest to the hydration front showed 52% more radial pressure than the axial swelling pressure.

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 score0.054
Threshold uncertainty score0.275

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 head0.019
GPT teacher head0.200
Teacher spread0.181 · 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

Citations6
Published2023
Admission routes2
Has abstractyes

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