Watershed Scale Transport Modeling – Using Nested Flow Models and Particle Tracking to Optimize Transport and Modeling Scale Minimize Computational Effort
Bibliographic record
Abstract
As part of an ongoing program to improve performance assessment methodologies, we have used detailed numeric models to simulate groundwater flow and contaminant transport for a hypothetical geological disposal repository located at depth within a generic Canadian Shield watershed. The geology consists of low permeability intact rock, penetrated with high-permeability fault zones. Deterministic modeling of the steady-state groundwater flow regime within the watershed was performed on the 10 km x 14 km subject area. Within this area, particle tracking was used to generate time-of-travel (TOT) maps at candidate depths ranging from 200 to 900 metres depth over the entire model domain. The TOT maps were used to help select an example repository location and depth, and to determine surface water discharge zones for this repository. Discretization constraints associated with modeling of the fracture zones and of repository spatial features limited the maximum element size. These restrictions, when coupled with numeric and execution time and memory constraints, dictated that the domain of the transport model be minimized. The practical solution was to use a nested approach, with a vault scale transport model embedded within a discharge zone scale transport model, embedded within a watershed scale flow model. Transport modeling was performed for three release locations corresponding to locations of hypothetical defective waste containers. Radionuclide mass fluxes to a water-supply well and to surface water discharge zones were calculated for each release location.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".