Disagreeing Evaluations for Slug Tests in Monitoring Wells: Importance of Standards
Bibliographic record
Abstract
Abstract Hundreds of variable-head permeability (slug) tests were carried out in monitoring wells to assess the hydraulic conductivity, K, of an unconfined sand-and-gravel aquifer and, after that, the migration of contaminants. The pollution case has generated a legal dispute about the loss of many water supply wells and major health issues. The experts for the defendants had analyzed the slug test data with methods that differ from the standards. For the legal issues, the author reanalyzed all test data and made all verifications as required by standards. The new K values were about three times higher than the initial values obtained without respect to the standards, a major difference. The divergence was solved by studying the statistical distributions of K values. The initial values were too low to explain the high K values obtained with pumping tests, but the new K values correctly explained them. The new K values predicted groundwater velocity values at least three times higher than the initial values predicted with K values obtained without following the standards. These higher velocities were confirmed by field tracer tests performed by independent companies that designed and operated field pilot tests for decontaminating groundwater. The capacity to reconcile medium-scale tests (slug tests in monitoring wells) and large-scale tests (pumping tests in large wells) forms new evidence that strengthens the reasons for why it is important to follow the standards and make the required verifications.
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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 | 0.147 | 0.410 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".