Harnessing the Power of Geophysical Imaging to Recharge California's Groundwater
Bibliographic record
Abstract
Abstract Motivated by the availability of 20,000 line‐km of airborne electromagnetic (AEM) data covering the Central Valley of California, we developed a workflow that uses resistivity profiles from electromagnetic (EM) data to assess the suitability of areas for groundwater recharge. We defined a suitable area as one where “fastpaths” of coarse‐grained material could efficiently move water from the ground surface to the water table. We defined recharge metrics and generated the corresponding maps by integrating resistivity profiles from AEM data, sediment type (from driller's logs), water level measurements, and water quality measurements. The workflow is publicly available through a web‐based application, fastpath ( https://fastpath.stanford.edu ). We produced maps displaying recharge metrics on a 400 m × 400 m grid covering the Central Valley, with 80% of the cells sufficiently close to an AEM resistivity profile (within ∼3 km) to be assessed for recharge. Various decisions are made in the workflow that result in a range of values for determined metrics at any given location. The maps summarizing all metrics show that between 19% (2,000,000 acres) and 56% (7,000,000 acres) of the total area in the valley is land suitable for recharge. The landcover with the largest total area of land classified as suitable is cultivated crops. We estimated the total space available for recharge water to be ∼170 km 3 which is two orders of magnitude greater than an estimate of the total volume of water likely to be available for recharge.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".