Using Electrical Resistivity Tomography (ERT) to Delineate Subsurface Structures at Siloam Hot Spring in the Soutpansberg Basin, South Africa
Bibliographic record
Abstract
Electrical resistivity tomography (ERT) was used to delineate subsurface structures at Siloam hot spring in the Soutpansberg Basin of South Africa. The spring water was reported to be hottest in South Africa. Groundwater with temperature ranging between 63.3 °C and 67.5°C was observed flowing from the spring. Relatively wet areas were noted to the south and north of the spring. Electrical resistivity arrays with Dipole-dipole and Wenner-Schlumberger configurations were used. Current injection and potential dipole separations of 5 m and 10 m were applied across the spring. Inversion results for arrays with the shorter dipole separation of 5 m had better resolution. Resistivity sections were generated from inversion of ERT data. The resistivity values for the low and high resistive zones were ρa < 10 and ρa > 70 Ωm respectively. Sub-horizontal resistivity zones were obtained from the inversion of Wenner-Schlumberger array survey data. High resistivity zones associated with dykes or sills were delineated on Dipole-dipole derived depth sections. Near vertical structures associated with water bearing faults or fractures were delineated at the Siloam hot spring.
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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 | 0.000 |
| 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".