Mapping Degrading Hydrocarbon Plumes with Self Potentials: Investigation on Causative Mechanisms using Field and Modeling Data
Bibliographic record
Abstract
Abstract Organic contaminants in ground water, such as those in landfill leachate or hydrocarbon spills, are of concern when they impact drinking water. Self potentials have been proposed as a rapid, simple and inexpensive method for mapping a plume from the ground surface as it is hypothesized they may be sensitive to gradients in the redox potential that develop around such plumes in some cases. We conducted self potential and resistivity surveys on two sites with hydrocarbon contaminant plumes in ground water and did not find a correlation between redox and self potentials, even after the electrical measurements were corrected for anthropogenic noise. To further explore the relationship between contaminant plumes and self potentials, we undertook modeling based on coupled fluxes. We used a finite element code and, as input, simulation results from a kinetic reactive transport model of a hypothetical hydrocarbon spill. Two types of source currents were tested. The first type is sources caused by diffusion, modeled using the Nernst-Plank equation. The diffusion model does not produce anomalies at the ground surface greater than 2 mV, which would be in the range of measurement error and within normal background variations in the field. The second type is sources caused by redox potential gradients, using an equation that assumes a transition zone where charge transfer is through a hypothetical electronic conductor, perhaps microbial nanowires or bioprecipitated minerals. For the case of the electronic conductor transition zone, the strength of the surface SP signal is affected by the electrical conductivity of the transition zone. The only scenarios that produced measureable self potential anomalies at the ground surface were those using the redox hypothesis with a transition zone that did not have very large electrical conductivity compared to background and strong Eh gradients.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".