Using flow dimension sequences to interpret non-uniform aquifers with constant-rate pumping-tests: A review
Bibliographic record
Abstract
Today it is still common practice to analyse pumping tests assuming Theissian conditions, resulting in interpretations that are at best grossly approximated, if not erroneous, with potentially negative impacts on the quality of water resource management. Over the last several decades, numerous technological advances in hydrogeology have been developed that make it possible to perform more realistic analyses of heterogeneous, non-purely Theissian flow systems (e.g. aquifers with non-uniform geometry and/or hydraulic properties). For this study a catalog of available behavioral flow models was compiled from the literature and consolidated into a unique interpretative scheme. This is based on two first-order flow modelling breakthrough developments derived from research works: the derivative analysis (Bourdet et al., 1983) and the flow dimension theory (Barker, 1988). Each derivative type-curve is segmented and converted into a sequence of stable flow dimensions, in order to integrate a large panel of models into a comprehensive conceptual hydrodynamic and interpretative framework. This allowed us to conduct a thorough discussion on a range of different hydraulic conditions and their associated most common responses to constant-rate pump tests, namely, the linear, bilinear, radial, and spherical flow regimes and various sequential combinations of these elementary flow regimes. The relevance of the catalogued behavioral flow models is examined based on the realism of their postulates and on their frequency of occurrence in the field. The proposed diagnostic methodology makes it possible to further refine the interpretation of pumping tests and to routinely detect complex aquifer conditions.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".