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Record W2173295313 · doi:10.1139/cgj-2012-0057

Effect of grain size on service life of MSW landfill drainage systems

2012· article· en· W2173295313 on OpenAlexafffundvenue
Yu Yan, R. Kerry Rowe

Bibliographic record

VenueCanadian Geotechnical Journal · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLeachateDrainageCloggingService lifeGeotechnical engineeringEnvironmental scienceInfiltration (HVAC)Drainage system (geomorphology)Environmental engineeringWaste managementGeologyEngineeringMaterials scienceComposite materialEcologyGeography

Abstract

fetched live from OpenAlex

A numerical model “BioClog-2D” is used to examine the service life and clogging of leachate collection systems with granular drainage material of different grain sizes. The modelling shows that the leachate characteristics at the end of the drainage pipe are significantly different from those in the leachate entering the leachate collection system and this reduction in leachate strength corresponds to an accumulation of clog mass within the saturated drainage layer. The calculated clog mass within the saturated drainage layer is dominated by the inorganic material, which is in encouraging agreement with field-observed data. The service life of leachate collection systems is increased with an increase in the grain size of the drainage material and decreased with an increase in the length of the drainage path. The service life of the drainage layer is shown to vary from a few years to over 100 years depending on the design of the system. The results indicate that in addition to the particle size of the granular material, the infiltration rate and leachate strength history greatly affect the estimated service life of leachate collection systems.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.007
GPT teacher head0.219
Teacher spread0.211 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations39
Published2012
Admission routes3
Has abstractyes

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