Comparison of electric fields induced in humans and rodents by 60-hz contact currents
Bibliographic record
Abstract
Contact currents flow when a conducting object such as an animal touches conductive surfaces at different potentials. This completes a path for current flow through the body. These currents provide an additional coupling mechanism between the human body and low-frequency external fields to that due to direct induction effects. Recent research indicates that childhood exposure to residential contact currents may play a role in explaining any possible association between residential magnetic fields and childhood leukemia. To verify this hypothesis, laboratory experiments with rodents are planned. Thus, it is important to understand the relationship between fields induced in rodents and humans. Results from numerical computations are reported here. They are based on high-resolution anatomically based inhomogeneous models of adult and child male humans and male and female rats and mice, for a variety of 60-Hz contact current scenarios. It is hoped that this work will aid in the design of experiments involving rodents and in the interpretation of results as applied to humans. It is found that for geometrically similar models, the induced electric-field scales in an anticipated inverse-square manner with the geometric scaling factor. For dissimilar models, scaling can provide a crude estimate for translating induced field results between species. However, numerical modeling provides the most suitable analysis tool for more accurate estimates.
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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".