Stochastic simulation of radionuclide migration in discretely fractured rock near the Äspö Hard Rock Laboratory
Bibliographic record
Abstract
We study the migration of sorbing tracers through crystalline rock by combining relatively simple transport measures with particle tracking in a discrete fracture network. The rock volume is on a 100 m scale and is a replica of a thoroughly characterized site at the Äspö Hard Rock Laboratory, Sweden. Flow is driven by generic boundary conditions consistent with the natural gradient in the region. The emphasis is on the global effect of fracture‐to‐fracture hydraulic variability where individual fractures are assumed to be of uniform aperture. The transport measures are conditioned on two random variables: the water residence time (τ) and a parameter which quantifies the hydrodynamic control of retention (β). Results are illustrated for two radionuclides: technetium (strongly sorbing) and strontium (weakly sorbing). It is found that the assumption of streamline routing or full mixing at fracture intersections has comparatively little impact on transport. The choice of the cubic or quadratic hydraulic law (i.e., relation between transmissivity and aperture) strongly affects water residence times but has little impact on average transport since it does not affect the statistics of β. If the statistics of β are known, then the distribution of water residence time (τ) is of little importance for transport. We assess the applicability of a linearized model β = τ/ b ret using two different approaches to estimate the effective “retention” aperture 2 b ret : from transmissivity data and from fracture density and flow porosity data. Under some conditions, these conventional estimates may provide acceptable representation of transport. The results stress the need for further studies on upscaling of τ, β distributions as well as on estimating effective parameters for hydraulic control of retention.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".