Simulation of head‐gradient‐coil induced electric fields in a human model
Bibliographic record
Abstract
A finite difference method was used to simulate the electric fields induced in the model by a gradient wire pattern. The pattern simulated corresponded to a design used to perform peripheral nerves stimulation experiments. The size (187.8, 169.02, and 150.24 cm tall) and position (brain and neck mode) of the model, relative to the magnet, as well as the voxel dimensions (3, 6, and 9 mm) of the model were varied to assess the effect on the simulation. The locations of stimulation reported from an experiment were classified according to nerve branch and compared with the peak-simulated electric fields. Model size and location affected the magnitude of the electric field, but not the position. Model resolution affected the location of the peak field. For the smallest resolution investigated, the nerves affected by the locations of peak stimulations in the model correlated to the frequency of stimulation in experiments. Although adequate resolution is required in order to assess the electric fields induced by gradient coil operation, the simulation of electric fields may be useful in evaluating gradient coil design prior to construction.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".