Three‐dimensional transient hydraulic tomography in a highly heterogeneous glaciofluvial aquifer‐aquitard system
Bibliographic record
Abstract
Hydraulic tomography has been proposed as an alternative site characterization method, however, relatively few field scale studies have been attempted. In this paper, we characterize the highly heterogeneous glaciofluvial aquifer‐aquitard system at the North Campus Research Site, located at the University of Waterloo, Waterloo, Ontario, Canada using transient hydraulic tomography (THT). In particular, we performed 9 pumping tests in a network of wells to image the hydraulic conductivity (K) and specific storage (Ss) distributions (or tomograms) as well as their uncertainties in three‐dimensions using the THT code of J. Zhu and T.‐C. J. Yeh (2005). We first performed stochastic inverse modeling of the 9 pumping tests individually to gain insight into the level of detail that can be imaged. Then, we sequentially included 4 of the pumping tests in a THT analysis. The resulting K and Ss tomograms were then validated visually by comparing them to stratigraphy and permeameter K estimates. The K and Ss tomograms were also rigorously assessed through the simulation of all 9 pumping tests and comparing the simulated and observed drawdowns. We find that performing the inversion with multiple pumping tests (i.e., hydraulic tomography) yields improved results when compared to the analysis of individual 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".