Monitoring managed aquifer recharge with electrical resistivity probes
Bibliographic record
Abstract
Abstract The use of managed aquifer recharge (MAR) to supplement groundwater resources can mitigate the risks to an aquifer in overdraft. However, limited information on subsurface properties and processes that control groundwater flow may lead to low levels of recapture of infiltrated water, reducing the efficacy of MAR operations. We used long 1D electrical resistivity probes to monitor the subsurface response over one diversion season at five locations beneath an operating recharge pond in northern California. The experiment demonstrated the benefits of integrating geophysical and standard hydrologic measurements. The water table response interpreted from time-lapse electrical resistivity images was in good agreement with traditional pore-pressure transducer measurements at coincident locations. Moreover, the electrical resistivity measurements were able to identify vertical variations in water saturation that would not have appeared in pore-pressure data alone. Changes in saturation estimated from electrical resistivity models indicated large hydraulic gradients at early time and suggested the presence of highly permeable conduits and baffles between the surface and the screened interval of recovery wells. The interpreted structure of these conduits and baffles would contribute to the movement of a large amount of infiltrated water beyond the capture zone of recovery wells before pumping begins, accounting in part for the low recovery rates.
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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 | 0.000 |
| 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 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".