Taking into Account Data Accuracy for Interpretation of Slug Tests in Confined or Unconfined Aquifers
Bibliographic record
Abstract
Abstract Different methods may be used to interpret the data of slug tests performed in aquifers, which are the water column height, Z, and time, t. The data accuracy usually is not taken into account. However, all measured Z data contain a random error and may contain a systematic error. This paper is believed to be the first one to explain how to assess the random error and display it in plots and, then, how to extract the systematic error, using three diagnostic graphs (two semi-log plots and a derivative plot that unifies all theories, and yields user-independent results). The plot of logZ versus t with “error” bars has a distinctive look: all Z data have the same “error,” but the smallest logZ data are the most inaccurate. As a result, the error bar is small at early times (large Z), but it increases to become very large at late times (small Z), which may modify the interpretation of data. Finally, the paper quantifies the errors that are made when using the Hvorslev equation for curved plots without error bars. Most often, the curvature results from a systematic error on the assumed piezometric level. When this error is not acknowledged, the user is at liberty to interpret the data and extract a hydraulic conductivity, K, which fits some beliefs. This yields a large error on K, which is quantified using equations and graphs.
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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 | 0.009 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".