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Record W4313404038 · doi:10.1680/jgein.22.00340

Performance of landfill low-permeability liners for minimizing groundwater contamination

2022· article· en· W4313404038 on OpenAlexaff
W. Hu, Yu Yan, R. Kerry Rowe

Bibliographic record

VenueGeosynthetics International · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsLeachateGeomembraneAquiferGeosynthetic clay linerGroundwaterPermeability (electromagnetism)Geotechnical engineeringEnvironmental scienceChlorideLimitingEnvironmental engineeringContaminationHydraulic conductivityWaste managementGeologySoil scienceMaterials scienceChemistryEngineeringSoil waterMembraneMetallurgy

Abstract

fetched live from OpenAlex

Low-permeability liners are required at the base of municipal solid waste (MSW) landfills to minimize leachate leakage and contaminant migration into groundwater. This paper uses a two-dimensional coupled groundwater flow and contaminant transport model to examine the performance of three types of low-permeability liners specified by the current Chinese landfill standard: (1) a compacted clay liner (CCL), (2) a geomembrane (GMB) overlying a CCL, and (3) a GMB overlying a geosynthetic clay liner (GCL) on a CCL. The model simulates leachate leaking and contaminant migrating over the entire base of the landfill for the CCL and through the holed GMB wrinkles for the GMB composite liners. The performance of each type of low-permeability liners was evaluated and compared in terms of leakage rate and peak impact of chloride on the aquifer. Based on liner cases and conditions examined in this paper, the results show that the three types of low-permeability liners are not equivalent for minimizing the leakage rate and chloride impact on the aquifer. The GMB + GCL + CCL performs the best among the three low-permeability liners, and is effective for limiting the peak chloride impact on the aquifer below the acceptable level in drinking water.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score0.998

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)0.0030.000

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 head0.011
GPT teacher head0.229
Teacher spread0.218 · 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.

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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueGeosynthetics InternationalSame topicLandfill Environmental Impact StudiesFrench-language works237,207