Monitoring managed aquifer recharge with electrical resistivity probes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it