Using the Velocity Graph Method to Interpret Rising-Head Permeability Tests after Dewatering the Screen
Bibliographic record
Abstract
Abstract This paper examines rising-head permeability tests performed on monitoring wells after the water level has been lowered down to the screened zone. Frequently, the usual semi-log graph appears as a set of two or three linear portions, making it difficult to assess the mean field hydraulic conductivity around the filter pack. Three tests, one in an unconfined aquifer and two in aquitards, are used to show that the usual semi-log graph can be curved either downwards or upwards. The velocity graph, which is linked to the conservation equation, clarifies what happens during and after dewatering the screened zone and the filter pack. In an unconfined aquifer, when the screened zone is close to the water table and dewatering is obtained by pumping, the curvature is due to the lowering of the water table before testing. Thus, the hydraulic conductivity must be calculated using a piezometric level lower than the pre-test value. In an aquitard, the curvature may be due either to an initial slow infilling of the dewatered filter pack (when it is too coarse to retain water by capillarity), or to an erroneous estimate of the piezometric level. The latter is due to the long time lag of the monitoring well and the natural drift of the piezometric level during the several testing days. In all cases, plotting the velocity graph clarifies what happens during the rising-head test.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".