Recent and New Information from the Slug Test Data of Ferris and Knowles (1954)
Bibliographic record
Abstract
Abstract The slug test theories take into account or not the tested material storativity, S. Ferris and Knowles (1954) proposed a first theory with S, the Ferris-Knowles (FK) theory. A few reasons were given to reject it in the 1970s. New and stronger reasons are given here. The FK test was incomplete because the data started 75 seconds after slugging with a large volume of water. These data are reinterpreted here with recent and correct methods, which have proven that the theories with S are wrong and cannot give an S value. The FK partial data yield a straight velocity plot, typical of all slug tests in aquifers, and a small piezometric error of 1 cm. During the first 75 seconds, 99 % of the water volume left the riser pipe to enter the aquifer. The only data (t > 75 s) were for the last 1 % of the water volume. The article shows that the FK theory yields an elastic S value corresponding to peat, which is nonsense. If the authors tested an aquifer, then their late data described the return to equilibrium of a water mound due to slugging 150 L of water and not the slug test data, which took place in the first 75 seconds. The delay in starting data collection may have been due to long dynamic effects with gas trapping and outgassing after slugging with a huge volume of water, but this was not documented. The correct methods for slug tests show that the FK theory tries to fit an exponential with a hyperbola. The FK theory was the first of weird methods trying to find the S value with confusing math and physics, which have so far delayed the use of physically correct methods in ASTM standards for slug tests.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".