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Record W2948539329 · doi:10.1139/cgj-2019-0058

Apparent salt diffusion coefficients for soil–bentonite backfills

2019· article· en· W2948539329 on OpenAlexvenueno aff
Kristin Sample‐Lord, Wenjie Zhang, Shan Tong, Charles D. Shackelford

Bibliographic record

VenueCanadian Geotechnical Journal · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsBentoniteSlurryLeaching (pedology)GeologySoil waterGeotechnical engineeringDiffusionClay mineralsSoil scienceMineralogyEnvironmental engineeringEnvironmental science

Abstract

fetched live from OpenAlex

Apparent diffusion coefficients, D a , were measured for two soil–bentonite (SB) backfills characteristic of those used in SB vertical cutoff walls for subsurface control of contaminant migration. The base soils for the backfills comprised either a natural lean clay or sand–bentonite mixtures. The base soils were mixed with 5% bentonite–water slurry to obtain a slump of 125 mm, resulting in total bentonite contents ranging from 4.76% to 7.31%. Values of D a for sodium chloride were measured using a recently developed dialysis-leaching test method. The D a values for the clay–bentonite and sand–bentonite backfills ranged from 2.5 × 10 −10 to 5.3 × 10 −10 m 2 /s and from 1.4 × 10 −10 to 8.1 × 10 −10 m 2 /s, respectively. Values of D a for both backfills increased with increasing average salt concentration in the specimen (C ave ). Values of D a decreased by ≤50% with increasing backfill bentonite content. For all C ave values, the clay backfills exhibited lower D a than the sand–bentonite backfills, likely due to additional fines from the lean clay. Results of this study enhance understanding of solute diffusion through SB cutoff walls, as well as support future use of the dialysis-leaching test method to measure diffusion properties of SB backfills.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.490
Threshold uncertainty score0.999

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)0.0050.001

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 head0.008
GPT teacher head0.218
Teacher spread0.210 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations18
Published2019
Admission routes1
Has abstractyes

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