Spontaneous potential surveys in surface fresh waters for engineering and environmental applications
Bibliographic record
Abstract
SUMMARY Geophysicists using the spontaneous potential method measure differences in electrical potential without providing an active source of current. Most spontaneous potential surveys have been carried out on land or in marine environments. In this paper, I evaluate the use of the spontaneous potential method in surface fresh water for small-scale environmental and engineering applications. In one survey reported here, the electrical potential between an electrode at the river edge and one suspended from a bridge was used to measure a high resolution profile across a river. In another, electrical potentials were measured between sets of electrodes mounted on a canoe. In both surveys, significant and consistent anomalies were detected particularly near bridge structures, and simple modelling in terms of point sources and line sources was undertaken to better characterize the causes of the anomalies. The possibility of an induction-induced voltage difference across the river caused by Earth’s magnetic field and flow in the river was also investigated. The absence of this potential is attributed to significant electrical conduction through the riverbed. This work demonstrates the utility of spontaneous potential as a technique for detecting and characterizing anomalies of environmental and engineering interest in fresh water environments.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".