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Record W4384652500 · doi:10.14796/jwmm.c502

A New Two-dimensional Dual-permeability Model of Preferential Water Flow in the Vadose Zone

2023· article· en· W4384652500 on OpenAlexvenueno aff
Charalampos Paraskevas, Christos Babajimopoulos

Bibliographic record

VenueJournal of Water Management Modeling · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsnot available
Fundersnot available
KeywordsRichards equationVadose zoneDiffusion equationFlow (mathematics)Water flowMechanicsInstabilityMatrix (chemical analysis)MathematicsPermeability (electromagnetism)Geotechnical engineeringGeologyMaterials sciencePhysicsChemistryWater contentGroundwaterEngineering

Abstract

fetched live from OpenAlex

LEAK2D (L2D) is a new, two-dimensional, dual-permeability model for the simulation of preferential water flow in the vadose zone, allowing for the continuous exchange of water between the matrix and the fracture domain. It is based on the two-dimensional Richards equation for the simulation of flow in the matrix domain and on the kinematic wave equation for the simulation of flow in the fracture domain. The Richards equation is solved by a combination of the Alternating Direction Implicit method and the Douglas-Jones predictor-corrector method. This combination leads to a very efficient, stable, and time-consuming method. A variable time step is used by which any instability of the numerical solution is avoided. The water transfer from the fracture to the matrix domain is estimated as a first-order approximation of the water diffusion equation. The model was used to satisfactorily simulate preferential flow under an extreme rainfall/irrigation event. The exchange of water between the two domains depends on parameters which have physical meaning; however, their exact values are difficult to be determined or measured. Based on the most common values of these parameters found in the literature, a sensitivity analysis was performed to define their effect on the output of the model.

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 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 score0.118
Threshold uncertainty score0.268

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)0.0000.000

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 head0.031
GPT teacher head0.241
Teacher spread0.210 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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