Peculiarities of Using 2D Electrical Resistivity Tomography in Caves
Bibliographic record
Abstract
The efficiency of archaeological studies inside caves could be greatly enhanced by geophysical methods because of their potential for examining deposit structure and features. Application of those methods in caves entails a number of problems caused by limited space for measurements and the complexity of the surrounding medium s structure as compared to above-ground measurements. In 2017, Selungur Cave in the Fergana Valley, Kyrgyzstan, was examined using electrical resistivity tomography. Because of the above concerns, in the course of the work the question of the reliability of the results arose. To clarify the issue, a numerical experiment was performed to assess the effect of the three-dimensional cave geometry on the results of a two-dimensional inversion. It was found that variations of cave geometry parameters result in unexpected false anomalies, and considerable errors in bedrock location and resistivity can occur. In the case of downward diverging cave walls, an accurate resistivity section can be obtained by using the inversion based on a two-dimensional model. Therefore, electrical resistivity tomography in caves with similar geometry can yield reliable results concerning the shape of bedrock surface, the thickness of sedimentary layers, and size and position of inclusions such as fallen fragments of roof therein.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".