Modelling and Installation of Underwater Electrodes for ERI Seepage Monitoring at an Embankment Dam
Bibliographic record
Abstract
Summary 3D time-lapse electrical resistivity imaging (ERI) is being trialled at the Mactaquac Dam to monitor seepage conditions near the interface between the embankment and an abutting concrete structure. Until recently the resistivity array consisted of five lines of electrodes running up the embankment’s downstream face, and across its crest. Measurements with that array resolved seasonal resistivity variations in the upper part of the dam’s clay till core that are consistent with zones of elevated seepage. To improve sensitivity below ∼10 m depth, the array has been expanded with underwater electrodes on the upstream face of the embankment. The underwater electrode layout was determined through many 2D and 3D modelling simulations. Concentrated seepage was simulated by changing the resistivity of specific zones in the core, and upstream rockfill shell, consistent with changes in the headpond. Synthetic resistivity data were then generated for predominantly pole-dipole measurement sequences mimicking those used at the dam. The synthetic data were inverted to see how well different electrode configurations could recover anomalies deeper in the core. After analysing many configurations, it was determined that adding underwater electrodes extending ∼1/3rd of the way down the upstream face would significantly improve sensitivity at depth in the core.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".