Reliable linear transformation of pseudo-pole-pole electrical resistivity datasets
Bibliographic record
Abstract
With a given fixed array of electrodes, a very large number of four-electrode measurements can be made. However, most of the possible measurements are not linearly independent, indicating that a smaller number of measurements can be made in order to save time, and the results of other measurements can be calculated later. In some cases, this calculation process can amplify measurement errors and so a robust quality control criterion is needed. In the past, it has been demonstrated that pole-pole, pole-dipole and pseudo-pole-dipole data can be used as a basis for calculating other measurements. In this study, we consider pseudo-pole-pole datasets as a basis. We define a pseudo-pole-pole array as one in which there is always a fixed current and potential reference electrode, but these reference electrodes are not necessarily far from the rest of the measurement array. We show that all four-electrode measurements can be easily calculated from pseudo-pole-pole data in ways that are similar to pole-pole data. We additionally show that good quality pseudosections and inversion results can be recovered from transformed data from pseudo-pole-pole data, especially for transformed data in configurations similar to the Wenner-α array. We also show that independent normal and reciprocal four-electrode measurements can be calculated for each four-electrode array and the comparison of these can be used as a quality control criterion. Our results show that a comprehensive dataset can be easily acquired from a pseudo-pole-pole survey which gives the geophysicist almost complete freedom of choice of array type to use in inversions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".