Hydraulic/Partitioning Tracer Tomography for DNAPL Source Zone Characterization: Small-Scale Sandbox Experiments
Bibliographic record
Abstract
Dense nonaqueous phase liquids (DNAPL) are prevalent at a large number of sites throughout the world. The variable release history, unstable flow, and geologic heterogeneity make the spatial distribution of DNAPLs complex. This causes difficulties in site remediation contributing to long-term groundwater contamination for decades to centuries. We present laboratory experiments to demonstrate the efficacy of Sequential Successive Linear Estimator (SSLE) algorithm that images DNAPL source zones. The algorithm relies on the fusion of hydraulic and partitioning tracer tomography (HPTT) to derive the best estimate of the K heterogeneity, DNAPL saturation (S(N)) distribution, and their uncertainty. The approach is nondestructive and can be applied repeatedly. Results from our laboratory experiments show that S(N) distributions compare favorably with DNAPL distributions observed in the sandbox but not so with local saturation estimates from core samples. We also found that the delineation of K heterogeneity can have a large impact on computed S(N) distributions emphasizing the importance of accurate delineation of hydraulic heterogeneity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 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".