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Record W4410980007 · doi:10.1016/j.envsoft.2025.106555

Towards simulating solute transport in complex, regional-scale fracture networks: a rapid upscaled approach

2025· article· en· W4410980007 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEnvironmental Modelling & Software · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsScale (ratio)Fracture (geology)GeologyGeotechnical engineeringGeographyCartography

Abstract

fetched live from OpenAlex

Currently, the most common approaches for simulating solute transport in fractured aquifers are the single- or dual-continuum and the discrete fracture network (DFN) methods. However, continuum approaches often lack accuracy due to averaging, whereas DFN approaches may be computationally prohibitive for large-scale fracture networks. To address these challenges, this study presents an Upscaled Fracture Network (UFN) model, developed by discretizing complex fracture networks into elementary volumes, identifying solute transport flow channels and calculating breakthrough curves within an elementary volume. The identified flow channels within the micro-scale DFN are collectively employed to construct the residence time at the macro-scale DFN. Validated against a random walk particle tracking (RWPT) DFN-based approach, the UFN model accurately captures solute transport processes in saturated fracture networks at the macro scale, and represents a significant advancement in simulating solute transport in complex, regional-scale aquifers due to its computational efficiency, simple implementation, and high level of accuracy.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.719
Threshold uncertainty score1.000

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 head0.019
GPT teacher head0.219
Teacher spread0.200 · 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