Mapping sediment type at a recharge site with a towed electromagnetic system
Bibliographic record
Abstract
With the goal of balancing demand for groundwater supply and demand, many water managers are intentionally recharging water into aquifers for later use by flooding agricultural fields, a practice called Ag-MAR. A major challenge with Ag-MAR implementation is understanding subsurface properties which may influence the quantity and quality of water recharged. We acquired data across a 138-acre orchard near Modesto, California, with a towed electromagnetic system, tTEM, from which we derived images of subsurface electrical resistivity to a depth of 42 m. We built on a previously developed workflow to that uses collocated resistivity values and sediment-type logs to obtain a subsurface model representing variation in the fraction of coarse-grained material (coarse fraction). We modified the workflow to reduce the uncertainty in resistivity and sediment type by averaging both of these values over larger depth intervals in determining the resistivity distribution that corresponded to each sediment type. We selected resistivity values to represent each sediment type to reduce the bias caused by the differences in sampled volumes in the input data. We compared our coarse-fraction model to sediment-type logs and observations of water table fluctuations during flooding experiments. Using the coarse-fraction model, we estimated the length of the potential recharge pathway from each 20 m x 20 m grid point at the ground surface to the water table, and the depth to the shallowest barrier to flow. We conclude that tTEM data captures the spatial variation in coarse fraction at a scale relevant to Ag-MAR.
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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 | 0.001 | 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) | 0.001 | 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".