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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 OpenAlex
Yang Liu, Will P. Gates, Abdelmalek Bouazza, R. Kerry Rowe

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

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 (k FL ) are compared with the values measured using a flexible-wall permeameter (k Tri ). Generally, the predicted hydraulic conductivity values are conservative (k FL /k Tri > 1) under water and low acid concentrations (≤0.015 mol/L). However, the back-calculated hydraulic conductivity is shown to be nonconservative (k FL /k Tri < 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.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.657
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0140.003

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