Barometric Fluctuations and Duration of Variable-Head (Slug) Field Permeability Tests
Bibliographic record
Abstract
Abstract Variable-head (VH) permeability tests are carried out in monitoring wells, driven permeameters, and between packers to assess local values of hydraulic conductivity. Most often, the water level position data are given versus time by a pressure transducer (PT) and an atmospheric pressure transducer (APT). Because the data vary with time, the PT and APT need to be synchronized. This article first shows what happens when a single correction for atmospheric pressure, patm, is used for all PT data for two slug tests, one in an aquifer, the other in an aquitard. Then, the article documents the patm fluctuation, including its maximum and minimum values during a given time, pmax and pmin, at a site and their statistical analysis for periods from 1 h up to 1 year, based on a 59-year data set. During a given time, the (pmax − pmin) value follows a lognormal distribution. For short testing times, typically less than 2 h, the patm value varies by less than 2 or 3 cm in 99 % of cases. The mean of the lognormal distribution increases with the observation time or test duration. The standard deviation is nearly constant for periods up to 30 days, and then decreases for periods from 1 month to 1 year. Synchronized data are needed to make a time-variable correction for all tests lasting more than 2 h, and the time-variable correction is the correct method for all slug tests, including short duration ones in aquifers.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".