Towards simulating solute transport in complex, regional-scale fracture networks: a rapid upscaled approach
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".