Estimating Flow Using Tracers and Hydraulics in Synthetic Heterogeneous Aquifers
Bibliographic record
Abstract
Regional ground water flow is most usually estimated using Darcy's law, with hydraulic conductivities estimated from pumping tests, but can also be estimated using ground water residence times derived from radioactive tracers. The two methods agree reasonably well in relatively homogeneous aquifers but it is not clear which is likely to produce more reliable estimates of ground water flow rates in heterogeneous systems. The aim of this paper is to compare bias and uncertainty of tracer and hydraulic approaches to assess ground water flow in heterogeneous aquifers. Synthetic two-dimensional aquifers with different levels of heterogeneity (correlation lengths, variances) are used to simulate ground water flow, pumping tests, and transport of radioactive tracers. Results show that bias and uncertainty of flow rates increase with the variance of the hydraulic conductivity for both methods. The bias resulting from the nonlinearity of the concentration-time relationship can be reduced by choosing a tracer with a decay rate similar to the mean ground water residence time. The bias on flow rates estimated from pumping tests is reduced when performing long duration tests. The uncertainty on ground water flow is minimized when the sampling volume is large compared to the correlation length. For tracers, the uncertainty is related to the ratio of correlation length to the distance between sampling wells. For pumping tests, it is related to the ratio of correlation length to the pumping test's radius of influence. In regional systems, it may be easier to minimize this ratio for tracers than for pumping tests.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".