Effects of monitoring and pumping well pipe capacities during pumping tests in confined aquifers
Bibliographic record
Abstract
This paper establishes how the water stored in the pipes of monitoring and pumping wells influences the drawdown curves of pumping tests in confined aquifers. Experimental and numerical results obtained with a physical model are first studied and then confirmed by field-test data. A large tank was used for fully controlled pumping tests. It contains a lower confined aquifer, an aquitard, and an upper unconfined aquifer. Pumping tests at a constant flow rate in the confined aquifer provided drawdowns that were analyzed for unsteady-state, steady-state, and recovery conditions. For a single monitoring well, the different interpretation methods provided similar values of transmissivity, T, and storativity, S. Drawdown curves gave much too high S values. These S values were equal to those resulting from water storage in the pipes of monitoring and pumping wells, according to the physical definition of storativity. The experimental T and S values were confirmed by two numerical analyses (finite elements) of the pumping test, one considering no water was stored in the pipes and the other considering stored water. Data of real pumping tests in confined aquifers confirmed that the S value calculated from drawdown curves can be influenced by water storage in monitoring and pumping wells for usual pipe diameters.Key words: pumping test, transmissivity, storativity, sandbox, in situ test, pipe capacity.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".