Towards Quantitative, Spatially Resolved Estimates of Dam Seepage by Time-Lapse Electrical Resistivity Imaging (ERI)
Bibliographic record
Abstract
Summary The Mactaquac hydroelectric generating station, located near Fredericton, New Brunswick, Canada has served as a test site for the development of geophysical dam condition monitoring techniques, for close to a decade. Efforts have focused on the region spanning the interface between the clay-till core of the dam and the wall of the adjacent concrete diversion sluice-way. Distributed temperature sensing (DTS) in a borehole drilled into the concrete suggests preferential seepage is present at relatively shallow depth. Since 2019 we have implemented time-lapse electrical resistivity imaging (ERI) – seeking to use seasonal changes in resistivity of the reservoir water as a tracer for imaging regions of preferential seepage through the core. Between April and December 2022, the resistivity of water in the reservoir varied by nearly a factor of four, being most resistive (∼300 Ohm-m) in the spring following snow melt, and most conductive (∼75 Ohm-m) in mid-August due to both elevated temperature and total dissolved solids (TDS). Order-of-magnitude estimates for seepage flux are found from the time lag between resistivity changes measured in the reservoir and correlated changes imaged in the core. Seepage estimates within the upper core are significantly higher than expected, corroborating prior inferences from the DTS system.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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 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".