A new coupled model for simulating the mapping of dense nonaqueous phase liquids using electrical resistivity tomography
Bibliographic record
Abstract
ABSTRACT Electrical resistivity tomography (ERT) has, for a considerable length of time, been considered promising for subsurface characterization activities at sites contaminated with dense, nonaqueous phase liquids (DNAPLs). The relatively few field studies available exhibit mixed results, and the technique has not yet become a common tool for mapping such contaminants or tracking mass reduction during their remediation. To help address this, a novel, coupled DNAPL-ERT numerical model was developed that can provide a platform for the systematic evaluation of ERT under a wide range of realistic, field-scale subsurface environments. The coupled model integrated a 3D multiphase flow model, which generates realistic DNAPL scenarios, with a 3D ERT forward model to calculate the corresponding resistivity response. Central to the coupling, and a key contribution, was a new linkage between the main hydrogeologic parameters (including hydraulic permeability, porosity, clay content, groundwater salinity and temperature, and air, water, and DNAPL contents evolving with time) and the resulting bulk electrical resistivity by integration of a variety of published relationships. Sensitivity studies conducted for a single node compared well to published correlations and for a field-scale domain demonstrated that the model is robust and sensitive to heterogeneity in DNAPL distribution and soil structure. A field-scale simulation of a DNAPL release and its subsequent remediation, monitored by ERT surface surveys, demonstrated that ERT is promising for mapping DNAPL mass reduction. The developed model provides a cost-effective avenue to test optimum ERT data acquisition, inversion, and interpretative tools, which should assist in deploying ERT strategically at contaminated sites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".