Development of Anatomically Realistic Whole-Body Models of Children and their Use in Electromagnetic Dosimetry
Bibliographic record
Abstract
Recently, there has been an increased concern regarding the effects of electromagnetic radiation emanating from wireless communication devices on the health of children. In order to determine safe levels of exposure, we intend to use computer simulations and numerical human models to estimate the specific absorption rate (SAR) in young children. However, only a few of the existing numerical models of young children are of the same quality and resolution as anatomically realistic adult models. Therefore, for accurate dosimetry in young children, we have developed anatomically realistic whole-body models using MRI data obtained from healthy three- and seven-year-old volunteers. Our model has a resolution of approximately 2 mm and is segmented into approximately 50 tissues and organs. First, we performed semiautomatic approximate segmentation using an image segmentation tool. Then, detailed segmentation was manually performed by health professionals. Finally, the positions and shapes of the segmented tissues were verified by a pediatric radiologist. The quality of our models is equal to or higher than that of adult models. Our models can be used to perform highly precise numerical simulations for studies on children. In this paper, we also present the basic SAR characteristics of our models at VHF/UHF frequencies.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".