Electromagnetic Inversion for Noninvasive Specific Absorption Rate Characterization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The inverse source framework, which comprises a subset of electromagnetic inversion, is applied to the noninvasive specific absorption rate (SAR) characterization problem. An algorithm is developed and presented which takes field measurements external to the phantom and provides the electromagnetic sources required to obtain the SAR distribution. The unique aspect of this inverse source algorithm is that it casts the problem as the simultaneous inversion (SI) of two sets of equivalent currents: one for the device under test (DUT), and the other for the phantom. The dependency of these two sets of currents is then incorporated as an explicit regularization term in the resulting algorithm. The method is proposed to be relatively robust in terms of measurement noise. A simplified two-dimensional problem is presented to support this proposition.
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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