Validation of hydraulic tomography in an unconfined aquifer: A controlled sandbox study
Bibliographic record
Abstract
Abstract In this study, we demonstrate the effectiveness of hydraulic tomography (HT) that considers variably saturated flow processes in mapping the heterogeneity of both the saturated and unsaturated zones in a laboratory unconfined aquifer. The successive linear estimator (SLE) developed by Mao et al. (2013c) for interpreting HT in unconfined aquifers is utilized to obtain tomograms of hydraulic conductivity (K), specific storage (Ss), and the unsaturated zone parameters (pore size parameter (α) and saturated water content (θs)) for the Gardner‐Russo's model. The estimated tomograms are first evaluated by visually comparing them with stratigraphy visible in the sandbox. Results reveal that the HT analysis is able to accurately capture the location and extent of heterogeneity including high and low K layers within the saturated and unsaturated zones, as well as reasonable distribution patterns of α and θs for the Gardner‐Russo's model. We then validate the estimated tomograms through predictions of drawdown responses of pumping tests not used during the inverse modeling effort. The strong agreement between simulated and observed drawdown curves obtained by pressure transducers and tensiometers demonstrates the robust performance of HT that considers variably saturated flow processes in unconfined aquifers and the unsaturated zone above it. In addition, compared to the case using the homogeneous assumption, HT results, as expected, yield significantly better predictions of drawdowns in both the saturated and unsaturated zones. This comparison further substantiates the unbiased and minimal variance of HT analysis with the SLE algorithm.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".