Interpreting Slug Tests with Large Data Sets
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Pressure transducers are frequently used to monitor slug tests, and to collect a data set for height Z of water column versus time t. Direct application of the data to draw a velocity graph usually produces a wide scatter, thus limiting the usefulness of the velocity graph method. This paper proposes to interpret the data set in five steps. First, a Z(t) plot is drawn to assess the uncertainty in the Z data. Second, a limited number of data points are selected to reduce the uncertainty to about ±10 % in the velocity graph. Third, the velocity graph is drawn to obtain the hydraulic conductivity k and the piezometric error H0, if any, giving the piezometric level for the test. Fourth, the graph of ln(Z−H0) versus t is drawn to verify its linearity and check the k value of the velocity graph. Fifth, the graph of Z versus log10(t) can be used in a curve fitting process to yield directly both H0 and k, thus providing a second method to interpret the data set, which may be the only one possible when the Z(t) data are inaccurate. Examples are provided to illustrate the five steps and common problems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it