Two Methods to Detect Poorly Sealed Monitoring Wells Using Pumping Test Data in a Confined Aquifer
Bibliographic record
Abstract
Abstract A correctly installed monitoring well (MW) has its riser pipe sealed against the borehole wall. When a MW is poorly installed, there is some vertical leakage close to the riser pipe, which creates a hydraulic short circuit (HSC). A static water level is measured in the pipe, but it is not the piezometric level in the aquifer, which is unknown. The piezometric error is the difference between the piezometric level and the static level in the pipe. It yields other errors in determining flow directions, travel times, and well capture areas. The groundwater sampled in the monitored aquifer may be viewed as polluted, whereas it is locally polluted by the faulty MW. This article deals with pumping tests in confined aquifers, for which a poorly sealed MW yields biased drawdown and recovery data. A few solutions to detect an HSC have been proposed, using either a slug test or a pumping test coupled with a tracer test. This article presents two new solutions to detect an HSC: they provide first the piezometric error and then the correct values for drawdown data. The data of a pumping test near Moncton, NB, are used to illustrate the two solutions. They show also that the HSC detection helps to solve previous inconsistencies between different sets of values for transmissivity, T, and storativity, S, as obtained by usual methods for pumping and recovery when short-circuiting is ignored or unsuspected.
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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 | 0.003 | 0.004 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".