Self‐potential for monitoring soil remediation by smouldering: a proof of concept
Bibliographic record
Abstract
ABSTRACT Near‐surface soils contaminated with non‐aqueous phase liquids, such as coal tar, crude oil, and chlorinated solvents, remain a serious problem. Smouldering remediation is a technique now being applied in the field for in situ destruction of non‐aqueous phase liquids. Based on a self‐sustaining exothermic reaction, smouldering remediation generates a hot region (>400 °C) that propagates through the subsurface. Self‐potential is here considered for the first time as a non‐destructive means for monitoring the smouldering remediation process. First, a series of sandbox experiments were conducted to investigate the magnitude of the thermoelectric coupling coefficient ( ) for different sand sizes, water contents, and heat sources. Measured values ranged from ‐0.47 mV/°C for coarse, water‐saturated sand to ‐0.05 mV/°C for fine sand with a saturation of 30%. Next, self‐potential measurements were conducted during several laboratory smouldering remediation experiments, examining the response as a function of both space and time. A significant self‐potential anomaly was observed on the surface during the smouldering period. Moreover, the magnitude of the self‐potential anomaly was demonstrated to be highly correlated to the separation distance between the (moving) reaction front and the (stationary) self‐potential electrode positions. Overall, this research suggests that the self‐potential method has a significant promise as a non‐invasive monitoring tool for in situ smouldering remediation of contaminated sites.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".