Vertical Discretization Impact in Numerical Modeling of Matrix Diffusion in Contaminated Groundwater
Bibliographic record
Abstract
Abstract Understanding the effects of contaminants that can diffuse into low‐permeability (“low‐ k ”) zones is crucial for effective groundwater remedial decision‐making. Because low‐ k zones can serve as low‐level sources of contamination to more transmissive zones over time, an accurate evaluation of the impacts of matrix diffusion at contaminated sites is vital. This study compared numerical groundwater flow and transport simulations using MODFLOW/RT3D at a hypothetical site using three cases, each with increasing discretization of the vertical 10‐m thick domain: (1) a coarse multilayer heterogeneous grid based on one layer for each of four different hydrogeological units, (2) a “low‐resolution” discretization approach where the low‐ k units were divided into several sublayers giving the model 10 layers, and (3) a “high‐resolution” numerical model with 199 layers that are a few centimeters thick. When comparing the results of each case, significant differences were observed between the discretizations used, even though all other model input data were identical. The conventional grid models (Cases 1 and 2) appeared to underestimate groundwater plume concentrations by a factor ranging from 1.1 to 36 when compared to the high‐resolution grid model (Case 3), and underestimated predicted cleanup times by more than a factor of 10 for some of the hypothetical sampling points in the modeling domain. These results validate the implication of Chapman et al. (2012), that conventional vertical discretization of numerical groundwater flow and transport models at contaminated sites (with layers that are greater than 1 m thick) can lead to significant errors when compared to more accurate high‐resolution vertical discretization schemes (layers that are centimeters thick).
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".