Time-lapse electrical resistivity anomalies due to contaminant transport around landfills
Bibliographic record
Abstract
The extent of landfill leachate can be delineated by geo-electrical imaging as a response to the varying electrical resistivity in the contaminated area. This research was based on a combination of hydrogeological numerical simulation followed by geophysical forward and inversion modeling performed to evaluate the migration of a contaminant plume from a landfill. As a first step, groundwater flow and contaminant transport was simulated using the finite elements numerical modeling software FEFLOW. The extent of the contaminant plume was acquired through a hydrogeological model depicting the distributions of leachate concentration in the system. Next, based on the empirical relationship between the concentration and electrical conductivity of the leachate in the porous media, the corresponding geo-electrical structure was derived from the hydrogeological model. Finally, forward and inversion computations of geo-electrical anomalies were performed using the finite difference numerical modeling software DCIP2D/DCIP3D. The image obtained by geophysical inversion of the electric data was expected to be consistent with the initial hydrogeological model, as described by the distribution of leachate concentration. Numerical case studies were conducted for various geological conditions, hydraulic parameters and electrode arrays, from which conclusions were drawn regarding the suitability of the methodology to assess simple to more complex geo-electrical models. Thus, optimal mapping and monitoring configurations were determined.
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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 | 0.000 |
| 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 | 0.000 |
| 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 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".