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Record W2598420921 · doi:10.1061/9780784480434.013

The Critical Role of Lateral Drainage Capacity in Limiting Leakage through a Low Permeability Geomembrane Cover

2017· article· en· W2598420921 on OpenAlexaboutno aff
Greg Meiers, Cody Bradley

Bibliographic record

VenueGeotechnical Frontiers 2017 · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPondingGeomembraneDrainageEnvironmental scienceGeotechnical engineeringHydrology (agriculture)Permeability (electromagnetism)Geology

Abstract

fetched live from OpenAlex

Waste storage facilities located near Sydney, Nova Scotia, Canada, were reclaimed with engineered cover systems. Similar alternative cover systems were utilized, each comprised of a geomembrane and overlying growth medium layer; however, a drainage layer above the geomembrane was not included in all instances. The monitored field performance of the alternate cover systems highlighted the detrimental impact that ponding of water above the geomembrane will have on performance. Empirical relationships for leakage through the geomembrane as a function of ponding pressure heads highlights that the performance of a low permeable layer is enhanced if adequate lateral drainage capacity is provided to eliminate sustained periods of water ponding. The field performance of the Hamburg-Georgswerder Landfill in Germany and the Monterey Peninsula Landfill in the United States are re-evaluated based on this finding. These case studies support the finding that the inclusion of a drainage layer mitigates risk associated with leakage attributed to geomembrane defects. However, the relative value of including lateral drainage capacity is also a function of site specific climatic conditions. Of particular importance in this regard is the intrannual and interannual variability in precipitation relative to evapotranspiration which will control the transient nature of the ponded conditions.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.004
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.021
GPT teacher head0.254
Teacher spread0.233 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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