Electrical Resistivity for Characterization and Infiltration Monitoring beneath a Managed Aquifer Recharge Pond
Bibliographic record
Abstract
Efficiency of managed aquifer recharge (MAR) via surface infiltration ponds relies heavily on the properties and processes of the unsaturated zone. The spatial and temporal resolutions needed in data for monitoring such processes are higher than typical hydrologic data can provide. Recently developed direct‐push resistivity probes can be located in the base of a MAR pond and used to obtain vertical electrical conductivity profiles with high spatial and temporal resolutions. In this study, we developed an inversion algorithm that uses a vertical electrical conductivity profile and auxiliary hydrologic data to estimate the van Genuchten parameters and saturated hydraulic conductivity of a homogeneous unsaturated zone. Using a synthetic case, we analyzed the method's accuracy and sensitivity to temporal and spatial resolutions in data. We then derived a new relationship for using the parameter estimation and electrical conductivity data to estimate infiltration rates and pond bottom clogging in situ in real time, extending electrical resistivity as a method for gaining qualitative infiltration information to a tool for quantitative infiltration rate monitoring. We found that we were able to best estimate the logarithm of the saturated hydraulic conductivity, which was within 5% of the true value for all cases. The van Genuchten parameter α was the least accurately predicted parameter, deviating at most 22% from the true value. We found that we could estimate infiltration rates and pond bottom clogging with a level of accuracy appropriate for use in modeling and management decisions, in most cases to within 11% of the true value.
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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 | 0.001 |
| 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 | 0.000 | 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 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".