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Record W2151829785 · doi:10.1139/cgj-2013-0241

Fluid loss as a quick method to evaluate hydraulic conductivity of geosynthetic clay liners under acidic conditions

2013· article· en· W2151829785 on OpenAlexaffvenue
Yang Liu, Will P. Gates, Abdelmalek Bouazza, R. Kerry Rowe

Bibliographic record

VenueCanadian Geotechnical Journal · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsQueen's University
FundersChina Scholarship Council
KeywordsPermeameterHydraulic conductivityGeosynthetic clay linerBentoniteLeachateGeotechnical engineeringConductivityGeosyntheticsHydraulic headSlug testMaterials sciencePore water pressureGeologyChemistrySoil scienceSoil waterEnvironmental chemistry

Abstract

fetched live from OpenAlex

This study investigates the performance of bentonite components of geosynthetic clay liners (GCLs) when exposed to aggressive leachates using the fluid loss test and provides a possible quick method for estimating the effect of acidic conditions on hydraulic conductivity. Fluid loss generally increases with increasing acid concentrations. Hydraulic conductivity values back-calculated from the fluid loss tests (kFL) are compared with the values measured using a flexible-wall permeameter (kTri). Generally, the predicted hydraulic conductivity values are conservative (kFL/kTri > 1) under water and low acid concentrations (≤0.015 mol/L). However, the back-calculated hydraulic conductivity is shown to be nonconservative (kFL/kTri < 1) at high acid concentrations (≥0.125 mol/L).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.020
GPT teacher head0.291
Teacher spread0.271 · 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 source (direct Gemma or distilled Codex), 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

Citations53
Published2013
Admission routes2
Has abstractyes

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