Time-lapse ERT monitoring of an injection/withdrawal experiment in a shallow unconfined aquifer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract To quantify performance of 3D time-lapse electrical resistivity tomography (ERT), a sequential injection/withdrawal experiment was designed for monitoring the pump-and-capture remediation of a conductive solute in an unconfined alluvial aquifer. Prior information is incorporated into the inversion procedure via regularization with respect to a reference model according to three protocols: (1) independent regularization involving a single reference model, (2) background regularization involving a reference model obtained via inversion of preinjection data, and (3) time-lapse regularization involving an evolving reference model obtained via inversion of data from previous experimental stages. Emplacement and sequential withdrawal of the solute is clearly imaged for all protocols. Time-lapse regularization results in greater amounts of model structure, while providing signifi-cant computational savings. ERT-estimated electrical conductivity is used to predict solute concentration and solute mass in the aquifer. At any experimental stage, we are able to estimate total solute mass in the aquifer with a maximum accuracy of 60%–85% depending on regularization protocol and survey geometry. We also estimate the withdrawn solute mass for every experimental stage (the change in mass between experimental stages). Withdrawn mass estimates are more reliable than total mass estimates and do not exhibit systematic underprediction or dependence on regularization protocol. Withdrawn mass estimates are accurate for changes in mass below 2–4kg of potassium bromide (KBr) for horizontal and vertical dipole-dipole surveys, respectively. Estimating the withdrawn solute mass does not require background subtraction and, thus, does not require background data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 it