Examples of Variable-Head Field Permeability Tests Used in Books: Given Interpretations and Correct Interpretations
Bibliographic record
Abstract
ABSTRACT When a monitoring well is tested for permeability, three methods, with three types of graphs, may be used to analyze the data of the water column Z(t) versus time t. The three graphs provide a clear diagnosis, previously proven to be user-independent. According to experience, there is usually a systematic error H0 on the Z(t) data, which has different origins. Statistically, most plots of log Z(t) versus t are curved upward, a few are curved downward, and very few yield a straight line. Positive or negative values of H0 yield upward or downward curvatures, whereas a null piezometric error yields a straight line. This article presents an analysis of 21 sets of slug test data found in textbooks with the three diagnostic graphs and obtain three new findings. First, the textbooks ignore the method already proven and implemented in other countries since the 1980s. Second, the books selected biased data because their plots of log Z(t) versus t are either curved upward or straight, but no plot is curved downward. Third, the data of the first test of the group 3 theory are abnormal and do not correspond to usual field data with good equipment. In addition, one book presents a test in an aquitard as an example of test in an aquifer. The H0 value was easily found by the optimization method for all tests, and the derivative graph for 19 of the 21 tests, two data sets being too inaccurate to yield a good derivative graph.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".