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Record W2111601562 · doi:10.1139/t05-039

Modelling species fate and porous media effects for landfill leachate flow

2005· article· en· W2111601562 on OpenAlexafffundvenue
Andrew Cooke, R. Kerry Rowe, Bruce E. Rittmann

Bibliographic record

VenueCanadian Geotechnical Journal · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsQueen's UniversityWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCloggingLeachatePorous mediumPorosityInertCalcium carbonatePrecipitationChemistryEnvironmental engineeringEnvironmental scienceChemical engineeringEnvironmental chemistryGeotechnical engineeringGeology

Abstract

fetched live from OpenAlex

A numerical, multiple-species, reactive transport model, coupled to models of kinetic biodegradation, precipitation, and particle attachment and detachment for predicting landfill leachate-induced clogging in porous media for one-dimensional flow systems, is described. The finite-element method is used for transport modelling, with reactions incorporated into point-source or sink terms. The species modelled include three volatile fatty acids, active and inert suspended biomass, dissolved calcium, and inorganic particles. The clog matter consists of active biofilm, inert biofilm, and inorganic solids. A biofilm model is used to simulate the growth and decay of active biomass and removal of substrate. Precipitate accumulation and calcium removal are simulated by a model of calcium carbonate precipitation. Interphase movement between clog matter and fluid includes the processes of attachment and detachment. A geometric representation of the porous media allows porosity and specific surface to be estimated from the thickness of the accumulated clog matter. The porosity of the media can thus change spatially and temporally. The behaviour of the model is demonstrated with a hypothetical example.Key words: clogging, landfills, leachate collection systems, modelling, biofilms, mineral precipitation.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.278
Threshold uncertainty score0.636

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 head0.015
GPT teacher head0.203
Teacher spread0.188 · 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 designSimulation or modeling
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
Published2005
Admission routes3
Has abstractyes

Explore more

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