How to Correctly Interpret Strange Data for Field Permeability (Slug) Tests in Monitoring Wells or between Packers
Bibliographic record
Abstract
ABSTRACT Falling-head and rising-head permeability tests have been carried out in monitoring wells, driven permeameters, and between packers for approximately one century. Recent tests are usually performed with a pressure transducer and an atmospheric pressure transducer, which should be synchronized, but this is rarely done. This article examines examples of strange test data for aquifers, due to field and human factors, and explains how to make an adequate interpretation. Many quality issues are listed, including poor decisions made by people who interpreted test data. Most often, a slug test has not a single quality issue but an assortment of interactive issues. Eight examples (1–8) are analyzed for (1) a too-small initial water column, (2) a shaky start when using compressed air, (3) inaccurate data for the water column height versus time, (4) variation in atmospheric pressure during the test, and (5–8) a few mixtures of listed issues with a poor estimate of the piezometric level for the test, which is ever-present. Clear plots illustrate each example. Explanations are given and listed on how to proceed to properly take into account quality issues for slug test data in aquifers. Clear rules are given to anyone who has to plan, perform, and interpret a slug test. Recommendations are made to improve a few standards and limit the risks of quality problems.
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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 | 0.029 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".