Detection of Heavy Metal and Hydrocarbon Contamination using a Miniature Resistivity Probe
Bibliographic record
Abstract
The usefulness of the electrical resistivity method for characterization of contaminated sites has been studied in many ways. The most commonly used device is a cone penetrometer that utilizes two or four electrodes to measure electrical resistivity (or conductivity) during a cone penetration test (CPT) along a vertical or horizontal alignment. This paper introduces a new miniature resistivity probe (MRP) that can potentially be deployed from a sampling platform to detect contaminant plumes prior to collecting soil samples. Following bench-scale tests aimed at quantifying the sensitivity of the MRP to various operating and environmental parameters, the response of the MRP in sandy soil containing various concentrations of tour heavy metals (Cu, Zn, Pb and Ni) and two hydrocarbons (phenol and gasoline) is evaluated. The test data revealed that the MRP has the potential to serve as an indexing tool for rapidly delineating contaminant plumes where heavy metals are present. The results for hydrocarbons were less conclusive, ranging from moderate ability to differentiate contaminated and non-contaminated soils for phenol to poor differentiation ability for gasoline.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 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".