Overcoming Signal-to-Noise Challenges With Pole-Dipole Resistivity Monitoring at a Hydroelectric Dam Site
Bibliographic record
Abstract
Summary Seasonal variations of resistivity are being monitored using 3D electrical resistivity imaging (ERI) at the interface between an earth-fill embankment and its concrete abutment to investigate the possibility of seepage along the boundary. The setting of these surveys is proximal to a large (660 MW) generating station which produces strong 60 Hz powerline noise. Pole-dipole arrays are being used to improve the depth of exploration available from relatively short survey lines. In the early stages, resistivity measurements lacked stability over periods of several seconds to minutes, especially at short offsets between current and potential dipoles where signal to noise ratios should be high. Comparing repeated short offset pole-dipole measurements to near-equivalent Wenner array measurements revealed that ambient noise across the ∼500 m long current dipole of our pole-dipole array was negatively influencing the stability of the square wave current applied by our low power (10 W) resistivity meter. Mitigating this issue through array changes and outlier rejection yielded highly repeatable results which inspire confidence for detection of subtle temporal resistivity variations within the embankment.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 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".