Simultaneous Extraction of Multiple Parameters From a Transmit–Receive Eddy Current Probe Above a Layered Planar Conductive Structure
Bibliographic record
Abstract
Abstract A validated analytical model of a transmit–receive coil pair situated above two parallel plates, separated by an air gap, was used as the basis for an inversion algorithm (IA) to extract probe liftoff, second layer plate resistivity, and plate-to-plate gap from multi-frequency eddy current data. The IA was tested over a large range of first layer wall thickness (3.80–4.64 mm), second layer plate resistivity (1.7–174 µΩ cm), second layer wall thickness (1.20–4.85 mm), probe liftoff (2.8–7.9 mm), and plate-to-plate gap (0–13.3 mm). At nominal liftoff (2.8 mm), the IA achieved a gap measurement accuracy of ±0.7 mm and was able to return good estimates of the second layer resistivity within ±1 μΩ cm for low resistivity samples, but with decreasing accuracy for higher resistivities. When the gap was fixed, the IA was able to measure changes in probe liftoff (relative to nominal) to an accuracy of ±0.2 mm. The reported accuracy and a demonstration for the ability to accurately estimate parameters outside of the calibration range provide confidence in the potential utility of the algorithm.
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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 | 0.000 |
| 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 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".