Simulated electrical conductivity response of clogging mechanisms for managed aquifer recharge
Bibliographic record
Abstract
ABSTRACT Motivated by the need for improved understanding and monitoring of clogging during managed aquifer recharge, we use numerical experiments to evaluate the effect of three different clogging mechanisms on electrical conductivity (EC), porosity, specific surface area, and electrical tortuosity of a simulated sediment pack. The clogging experiments are designed to simulate effect of clogging due to: (a) addition of finer grains, (b) addition of nonconductive films, and (c) addition of conductive films. The simulations involved starting with a random grain pack of 43% porosity, and subsequently reducing the porosity as would occur during clogging. For each of the experiments, we compute the EC response, specific surface area, and electrical tortuosity across the range of porosities. The differences in EC response between (a) and (b) is minor, however, the sediment parameters measuring pore-space configuration show very different responses (i.e., specific surface area and tortuosity), indicating EC is limited in its sensitivity to specific pore configurations. The results from simulations (a) and (b) are well described by Archie’s equation. For the conductive film experiments (c), we explore the effect of film growth for four different surface conductivities ranging from 1 Sm−1 to 7 Sm−1. These conductivities correspond to a range of 5–35 times more conductive than the pore fluid conductivity. The bulk EC signal for each of the films results in a distinct manifestation in terms of measured bulk EC. We fit the EC response of the conductive film experiments with a model based on volume fraction occupied by the film; although the model fit the observed results, we required a unique set of fitting parameters for each Film conductivity.
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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 | 0.001 | 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 | 0.000 |
| 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 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".