What maximum permeability can be measured with a monitoring well?
Bibliographic record
Abstract
The PVC screens of recent monitoring wells (MWs) have thin slots and a low open area, usually in the 2–8% range. The MW screen and filter pack may cause important head losses which are not taken into account when interpreting the data of permeability tests performed using the MW. The equivalent hydraulic conductivity K of usual PVC screens was defined by hydraulic tests in a water tank, which have shown that gas micro-bubbles, a common problem in MWs and filter packs, contribute to increase the parasitic head losses. Closed-form equations and numerical models are used to explain by how much a field permeability test in a MW under evaluates an aquifer K value due to parasitic head losses in the screen and filter pack. The MW can properly measure the local soil K value only if it is markedly lower than the maximum MW value as obtained in a water tank. The MW measuring capacity can reach 5 × 10− 3 m/s for large slots and deaired water, but is most often between 10− 5 and 10− 4 m/s for small slots in field conditions, and it can be only 10− 6 m/s for poorly designed and installed MWs. The limited measuring capacity may yield artificial permeability scale effects as often registered in environmental studies.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.011 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.001 |
| 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".