Consideration of magnetically‐induced and conservative electric fields within a loaded gradient coil
Bibliographic record
Abstract
We present a method to calculate the electric (E)-fields within and surrounding a human body in a gradient coil, including E-fields induced by the changing magnetic fields and "conservative" E-fields originating with the scalar electrical potential in the coil windings. In agreement with previous numerical calculations, it is shown that magnetically-induced E-fields within the human body show no real concentration near the surface of the body, where nerve stimulation most often occurs. Both the magnetically-induced and conservative E-fields are shown to be considerably stronger just outside the human body than inside it, and under some circumstances the conservative E-fields just outside the body can be much larger than the magnetically-induced E-fields there. The order of gradient winding and the presence of conductive RF shield can greatly affect the conservative E-field distribution in these cases. Though the E-fields against the outer surface of the body are not commonly considered, understanding gradient E-fields may be important for reasons other than peripheral nerve stimulation (PNS), such as potential interaction with electrical equipment.
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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) | 0.001 | 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".