Automatic detection of detached and erroneous electrodes in electrical impedance tomography
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Bibliographic record
Abstract
One unfortunate occurrence in experimental measurements with electrical impedance tomography is electrodes which become detached or poorly connected, such that the measured data cannot be used. This paper presents an automatic approach to detect such erroneous electrodes. It is based on the assumption that all valid measurements are related by the image reconstruction model, while the measurements from erroneous electrodes are unrelated. The method estimates the data at an electrode based on the measurements from all other electrodes, and compares it to the measurements. If these data match adequately, the set of electrodes does not contain an erroneous electrode. In order to detect an erroneous electrode amongst N electrodes, all sets of N-1 electrodes are tested, and the set with the best match between measurements and estimate is identified as the one which excludes the erroneous electrode. The method was tested on simulated and experimental data and showed consistent identification of erroneous electrodes with those made by experts.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it