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Modeling of Leachate Characteristics and Clogging of Gravel Drainage Mesocosms Permeated with Landfill Leachate

2012· article· en· W2170275258 on OpenAlexafffund
R. Kerry Rowe, Yu Yan

Bibliographic record

VenueJournal of Geotechnical and Geoenvironmental Engineering · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLeachateCloggingMesocosmDrainageEffluentEnvironmental scienceEnvironmental engineeringGeotechnical engineeringHydraulic conductivityWaste managementGeologySoil scienceChemistrySoil waterEngineeringEcology

Abstract

fetched live from OpenAlex

A two-dimensional numerical model, BioClog, is used to estimate the leachate characteristics and leachate-induced clogging of laboratory mesocosms permeated with real municipal solid waste landfill leachate. The model is used to examine mesocosms with 38-mm (nominal diameter) gravel subjected to different durations of leachate permeation, mesocosms run-in series, and mesocosms with 19-mm gravel. A comparison of the calculated and measured leachate concentrations indicates that the model provided reasonable predictions of the effluent chemical oxygen demand (COD) and calcium concentrations. The calculated porosities within the saturated drainage layers are general in encouraging agreement with the measured values from all experimental mesocosms. The calculated and measured hydraulic conductivities are in reasonable agreement. The modeling results indicated that reducing the mass loading for the drainage layers and increasing the particle size of granular media can be expected to extend the time before clogging of drainage layers, and therefore, to extend the service life of 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.638
Threshold uncertainty score0.674

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)0.0000.000

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 head0.006
GPT teacher head0.178
Teacher spread0.172 · 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

Citations34
Published2012
Admission routes2
Has abstractyes

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