Electric Fields in the Human Body due to Electrostatic Discharges
Bibliographic record
Abstract
Electrostatic discharges (ESDs) occur when two objects at different electric potentials come close enough to arc (spark) across the gap between them. Such discharges may be either single-event or repetitive (e.g., 60 Hz). Some studies have indicated that ESDs may be a causative factor for health effects in electric utility workers. Moreover, a hypothesis has recently been forwarded imperceptible contact currents in the human body may be responsible for health effects, most notably childhood leukemia. Numerical modeling indicates that the electric fields in human tissue resulting from typical contact currents are much greater than those induced from typical exposures to electric and magnetic fields at power line frequencies. Numerical modeling is used here to compute representative spark-discharge dosimetry in a realistic human adult model. The frequency-domain scalar potential finite difference method is applied in conjunction with the Fourier transform to assess electric fields in selected regions and tissues of interest in the body. Electric fields in such tissues as subcutaneous fat (where peripheral nerves may be excited), muscle and bone marrow are of the order of kilovolts per meter in the lower arm. The pulses, however, are of short duration (approximately 100 ns).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".