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Record W2110033265 · doi:10.1139/t2012-052

Modelling leachate-induced clogging of porous media

2012· article· en· W2110033265 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
KeywordsCloggingLeachateDrainageEnvironmental scienceGeotechnical engineeringSettlingSand filterPorous mediumHydraulic conductivityEnvironmental engineeringWaste managementGeologyPorositySoil scienceWastewaterEngineeringSoil water

Abstract

fetched live from OpenAlex

A numerical model to predict biologically induced clogging of municipal solid waste leachate collection systems is described. The model simulates the accumulation of clog mass in the porous media by the growth of biomass and precipitation of minerals. In addition, the settling and deposition of suspended particles is modelled. A technique for modelling filter-separator layers between the waste and the coarse granular drainage material is described. The application of the model is illustrated for two series of laboratory mesocosm experiments: one where the waste was in direct contact with the underlying drainage layer and the second where there was a granular filter between the waste and the coarse gravel drainage layer. The modelling shows that the clogging of the gravel in the lower regions of the saturated drainage layer is estimated better by the advanced numerical model than the previously published model. In both cases, the calculated results are in encouraging agreement with the observed behaviour. It is concluded that this model has potential for use in modelling biologically induced clogging of municipal landfill 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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.493
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.229
Teacher spread0.199 · 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

Citations46
Published2012
Admission routes3
Has abstractyes

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