A Rapid Screening‐Level Method to Optimize Location of Infiltration Ponds
Bibliographic record
Abstract
A rapid-screening technique was developed to identify lithologies that best disperse artificial recharge via surface infiltration and minimize effects on ground water chemistry. The technique prospectively evaluates basin infiltration rates and water chemistry influences by integrating geotechnical, hydraulic, and water quality data with column test data and numerical modeling. The technique was validated using field data collected from surface infiltration basins designed to recharge ground water pumped from the Pipeline pit gold mine in Nevada. Observed recharge rates at these infiltration sites correlated most significantly with depth to groundwater, with basins in coarse-grained lithologies performing better (0.45 to 0.85 m/day) than those with fine-grained layers (< 0.30 m/day). Observed water quality resulting from leaching of the previously unsaturated vadose zone showed a transitory (< six months) increase in solute concentrations followed by a decrease to baseline conditions, a phenomenon also observed in column tests that leached native soils with local ground water. Leaching of fine-grained soils with evaporites resulted in greater solute concentrations (TDS > 2000 mg/L) than coarse-grained soils (< 1200 mg/L). The results of HYDRUS_2D simulations using the accumulated data as input were in agreement with observed ground water chemistry downgradient of the infiltration basins for a variety of lithologies. Sites for infiltration basins can be rapidly screened to include areas with greatest depth to groundwater and in coarsest alluvial sediments, and impact to ground water chemistry can be reliably predicted using computer modeling and column test results.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".