Tracer Tests in Stratified Alluvial Aquifers: Predictions of Effective Porosity and Longitudinal Dispersivity versus Field Values
Bibliographic record
Abstract
Abstract Tracer tests in aquifers are key tests to delineate protection perimeters around drinking water wells. They help to determine field values of effective porosity, ne, and longitudinal dispersivity, αL, from curve fitting to a breakthrough curve (BTC). It is difficult to predict ne, but correlations obtained with field or numerical tracer tests may be used to predict αL. The BTCs of field tracer tests differ from those predicted by the advection-dispersion theory in three ways: (1) early arrival with smaller than expected ne, (2) scale-dependent αL, and (3) a long thick tail. In this article, physical principles are used to obtain new closed-form predictive equations for ne and αL in stratified alluvial aquifers. The new equations give ne and αL for the hydraulically equivalent homogeneous aquifer. The predicted values for ne are shown to fit the field values of seven well-documented field tracer tests. The new equations explain the ne field values and can explain field values of αL for stratified aquifers, their variation with distance, and the variance of the ln(K) distribution. If the tracer is injected for a limited time, the predicted BTC also displays the three usual features of field data, which simply result from a lognormal K-distribution. The new equations and their experimental verification correctly elucidate some difficulties due to aquifer heterogeneity and improve our ability to predict groundwater movements in the subsurface.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".