Simple Resistivity Probe System for Real‐Time Monitoring of Injected Reagents
Bibliographic record
Abstract
Abstract In situ treatment usually requires contact between an injected reagent and target contaminant to realize mass removal from source zones and plumes. Despite significant site characterization efforts, unknown heterogeneities that exist at all spatial scales often hinder prediction of the distribution of injected reagents. To provide remediation practitioners with additional information on the distribution of injected reagents, a prototype real‐time monitoring probe was designed and tested. This new probe comprises a two‐wire resistivity circuit, hence the designation dipole resistivity probe (DRP). The low‐cost DRPs were built to be attached as arrays to a central stalk, and installed by direct‐push techniques. An installed network of multilevel DRPs can be used to detect the arrival, persistence, and relative concentration of a high‐conductivity reagent solution in real‐time across a zone of interest. Static cell and sandbox experiments were conducted to test and refine the DRP design before field testing. Static cell experimental results indicated that the DRP was sensitive to solution electrical conductivity (EC), and that the probe response and EC relationship is nonlinear and dependent on the fixed resistor used. The choice of fixed resistor can be adjusted to optimize the DRP response over a critical EC range of interest. Under dynamic conditions in the sandbox, the DRP was able to reproduce breakthrough profiles collected by a commercial EC sensor. The results from two field studies demonstrated the utility of the DRPs to generate information regarding the arrival and persistence of reagents in an efficient and cost‐effective manner. The first field study used a network of DRPs to monitor the land application of a sodium sulfate solution, and the second field study employed DRPs to monitor the distribution and longevity of a persulfate solution. While additional field testing is warranted, the results presented are encouraging and suggest that this low‐cost system can be used to improve our understanding of the detailed migration of injected reagents in treatment zones.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".