Effect of hydrocarbon contamination on streaming potential
Bibliographic record
Abstract
In the last decade, spontaneous potentials have been used to map reduced groundwater contaminant plumes. The measured signal includes contributions of both the streaming potential and the geo‐chemical electrical potential and for these applications the geochemical electrical potential is of interest. Therefore, the streaming potential must be modelled and subtracted from the measured spontaneous potential. Streaming potential is caused by the displacement of ions in the electrical double layer on sediments due to hydraulic head gradients in the fluid within a porous medium. Commonly, the streaming current coupling coefficient is assumed to be constant when modelling the streaming potential both inside and outside a contaminant plume. We postulated that organic contaminants might change the sediment surface properties, thereby affecting the streaming potential signature. To test this hypothesis, we used sediment and groundwater samples from hydrocarbon impacted field sites. Samples were equilibrated with water from the site for several weeks before the tests. Each sample was split into two and tested with clean water and hydrocarbon polluted water in an apparatus constructed following the design of Sheffer ( ). The streaming current coupling coefficient of the polluted sub‐samples was lower than that of the unpolluted sub‐samples for five of the six samples tested. Other parameters measured were hydraulic conductivity, cation exchange capacity and for the fluid: electrical conductivity, pH, major ions and hydrocarbons. Based on the values of the laboratory experiments, numerical modelling was used to demonstrate that the impact of these changes on field measurements is negligible.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".