Single Data Set Calibration and Imaging with Uncooperative Electromagnetic Inversion Systems
Why this work is in the frame
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Bibliographic record
Abstract
Working with a multi-static grain-bin Electromagnetic Imaging (EMI) system, we propose a novel calibration procedure where we calibrate out the measurement system effects and image on a single set of S-parameters. Unlike most EMI systems, which require two measurements, only one set of S parameters are used for both calibration and imaging. The procedure relies on performing a parametric inversion to estimate the bulk contents of the grain bin, followed by an optimization to obtain per-channel calibration coefficients. The core assumption of the calibration procedure is that cross-talk between channels is small enough to ignore. A proof-of-concept of the calibration and imaging procedure is given with a simple synthetic example of an existing grain bin. The synthetic example shows that under highly favorable assumptions the single-data set procedure can give results of similar quality to the traditional (two data set based) calibration and imaging procedure.
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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 | 0.000 |
| 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 it