Influence of Piezometer Construction on Groundwater Sampling in Fractured Rock
Bibliographic record
Abstract
A numerical model for groundwater flow and solute transport was employed to examine the influence of the screen and sandpack on the collection of a representative geochemical sample from a piezometer monitoring well installation in a discretely fractured bedrock aquifer. The optimization of screen and sandpack combinations was explored for the potential to reduce purging times and volumes in practice. Simulations accounted for the location of the fractures along the well screen, fracture aperture, screen length, and the pumping rate. The variability in the required purging times (t(99)-the time required to achieve 99% fractional contribution from the formation to pump discharge) can be explained by: (1) the relative hydraulic conductivities of the components of the system (fracture, sandpack, and screen), (2) the truncation of the flow field from the fracture to the screen by the upper and/or lower boundary of the sandpack of the flow field from another fracture, and (3) time-dependent drawdown. During pumping, only a portion of the sandpack may actually become hydraulically active. The optimal configuration (shortest purging time) is achieved when the ratios of the screen, sandpack, and fracture hydraulic conductivities are close to 1:1:1. More importantly, the role of the fracture hydraulic conductivity in the ratios is not as crucial to reducing t(99) as having the hydraulic conductivities of the screen and sandpack as similar as possible. This study provides a better understanding of well dynamics during pumping for the purpose of obtaining representative groundwater samples.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 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".