Electrical Conductivity Probes for Studying Vadose Zone Processes: Advances in Data Acquisition and Analysis
Bibliographic record
Abstract
We have developed a direct‐push probe that can be permanently installed and used to continually monitor changes in the electrical conductivity of the vadose zone. We present an inversion processing workflow for producing images of electrical conductivity as a function of time from the measured resistances. We monitored time‐lapse changes in the sediments directly beneath an artificial recharge pond to obtain information about the controls on the decreasing infiltration rates and associated clogging in the recharge pond. Three probes, placed at a depth of 2 m below the bottom of the pond, were monitored for 120 d, with data being acquired every 18 min. Through the use of one‐ and two‐dimensional inversions, we identified what we believe to be a disturbed zone immediately adjacent to the probes. Based on an assessment of the error in the one‐ and two‐dimensional models, however, we chose to work with the one‐dimensional inversion results, converting them from electrical conductivity estimates to estimates of saturation as a function of time. The saturation maps show clear evidence of clogging. In particular, at late time, we could see that a thin layer (<10 cm), possibly associated with biological activity, controlled infiltration. Our ability to obtain high‐resolution spatial and temporal sampling of the subsurface with electrical conductivity probes offers a new approach to acquiring data about properties and processes in the vadose zone. The permanent installation of such probes could provide valuable information about natural processes governing infiltration and recharge and aid in the operation of engineered 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".