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Record W1818872831 · doi:10.1139/cgj-2014-0436

Reactive transport of chemicals in unsaturated soils: numerical model development and verification

2015· article· en· W1818872831 on OpenAlexvenueno aff
Majid Sedighi, Hywel Rhys Thomas, Philip J. Vardon

Bibliographic record

VenueCanadian Geotechnical Journal · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research Council
KeywordsDissolutionSoil waterPorosityGroundwaterFlow (mathematics)Geotechnical engineeringEnvironmental scienceGeologySoil scienceMechanicsChemical engineeringEngineering

Abstract

fetched live from OpenAlex

This paper presents the development of a numerical model for reactive transport of multicomponent chemicals in unsaturated soils. The model has been developed based on a coupled thermal, hydraulic, chemical, and mechanical (THCM) formulation, and extended by the inclusion of geochemical processes under mixed equilibrium and kinetically controlled reactions in–between the solid, aqueous, and gas phases in soil. This has been achieved by coupling the transport model, COMPASS, with the geochemical model, PHREEQC. Key coupling between the geochemical modelling and the flow of chemicals has been established via the inclusion of porosity modification from mineral precipitation–dissolution reactions and the consequential effects on flow processes. Verification of the developed model is addressed via a series of benchmark simulations with a focus on testing the coupling between the transport model and geochemical model. Good results have been achieved for the verification of the theoretical and numerical implementation of the new developments in the model. A simulation is presented to demonstrate the effects of mineral reactions on porosity evolution and chemical diffusion in a low porosity soil. The model developed is an advanced tool for studying the hydrogeochemical processes in unsaturated soils under variable THCM 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)0.000
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 score0.671
Threshold uncertainty score0.595

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 head0.024
GPT teacher head0.224
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.

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

Citations28
Published2015
Admission routes1
Has abstractyes

Explore more

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