HUMAN BODY EXPOSURE TO POWER LINES: RELATION OF INDUCED QUANTITIES TO EXTERNAL MAGNETIC FIELDS
Bibliographic record
Abstract
Induced electric field and corresponding current density values in various organs of the human body can be computed numerically using a heterogeneous, anatomically representative voxel model. Such computations are available for uniform magnetic fields of various directions with respect to the body. The highest exposure levels occur for non-uniform fields, most often in occupational settings. Various organ induced dosimetric measures of the induced quantities can also be computed, although the associated computational complexity and effort are greater than for uniform fields. A simplified method of estimation of the induced measures is described and validated. The method is based on evaluation of the external (exposure) magnetic flux density in locations corresponding to those occupied by various organs and dosimetry for the uniform fields. Computations of the external fields are relatively simple even for very complex geometries of current-carrying conductors. Computational methods are available for external fields. The external magnetic fields can also be measured. Detailed organ dosimetry is already published. In this contribution, the proposed simplified dosimetry is verified using accurate, numerically computed dosimetry for four non-uniform field exposure scenarios. For most dosimetric measures and organs, the proposed method gives conservative estimates. Only in rare cases when a large organ is in a weak exposure field compared to the whole-body average exposure, the induced dosimetric measures may be underestimated by up to 10%. Another exception is the maximum induced electric field in spatially distributed tissues such as bone marrow, muscle, or skin when a part of the limb is in a very strong magnetic field close to the conductor. However, both of these situations are easily recognizable from the mutual configuration of the human body and the current-carrying conductors. Thus, additional corrections can be applied to the 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 | 0.002 |
| 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".