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Record W3001443281 · doi:10.1029/2019gl086879

Characterization of Pore Water Flow in 3‐D Heterogeneous Permeability Fields

2020· article· en· W3001443281 on OpenAlexaff
Xiaolong Geng, Michel C. Boufadel, Kenneth Lee, Chunjiang An

Bibliographic record

VenueGeophysical Research Letters · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsConcordia UniversityBedford Institute of OceanographyFisheries and Oceans Canada
Fundersnot available
KeywordsAquiferMultifractal systemVortexAdvectionGroundwater flowGroundwaterGeologyPorous mediumPermeability (electromagnetism)Soil scienceGeophysicsMechanicsHydrology (agriculture)PorosityFractalGeotechnical engineeringPhysicsThermodynamicsMathematics

Abstract

fetched live from OpenAlex

Abstract Subsurface heterogeneity could influence groundwater flow with implications on structure and productivity of aquifer ecosystems. Here we investigate effects of multifractal heterogeneity on topology of groundwater flow through MODFLOW simulations within a Monte Carlo framework. The results show that heterogeneity leads to focused groundwater advection and the creation of hotspots of bending‐type vortex flow in the fields. We demonstrate for the first time that the vortex structures characterized by Q criterion are 3‐D distributed and greatly deform surrounding pore‐water flow. The structures exhibit scale‐invariant features in multifractal fields and in stationary fields below the correlation scale, indicating that such vortex flow might be widely present with no characteristic scale. Complex spatial patterns of kinetic energy dissipation rate are identified for pore water flowing through heterogeneous porous media and correlate strongly with preferential flows. These findings are important for understanding solute fate and transport in aquifer 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 head0.030
GPT teacher head0.270
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), 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

Citations19
Published2020
Admission routes1
Has abstractyes

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