Three-dimensional modelling of streaming potential
Bibliographic record
Abstract
The self-potential (SP) method responds to the electrokinetic phenomenon of streaming potential and has been applied to hydrogeologic and engineering investigations to aid in the evaluation of subsurface hydraulic conditions. To enable the study of variably saturated flow problems of complicated geometry, a 3-D finite volume algorithm is developed to evaluate the SP distribution resulting from subsurface fluid flow. The algorithm explicitly calculates the distribution of streaming current sources and solves for the SP given a model of hydraulic head and prescribed distributions of the streaming current cross-coupling conductivity and electrical conductivity. The forward solution is verified by comparing it with an analytical solution for a point source of flow and measured data taken at the surface of a homogeneous embankment. We apply the forward model to a synthetic pumping well example to illustrate that heterogeneous physical property distributions can result in significant charge accumulation. The sign and magnitude of this secondary charge is determined by the physical property and potential gradients at the interface, and can complicate the interpretation of SP data when a single primary flow source is assumed. The 3-D character of the SP response to seepage through an embankment and foundation is illustrated in a preliminary study of SP data collected at a dam site in British Columbia.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".