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Record W2162874540 · doi:10.1109/iembs.2009.5333598

Estimation of variability of specific absorption rate with physical description of children exposed to electromagnetic field in the VHF band

2009· article· en· W2162874540 on OpenAlexfundno aff
Tomoaki Nagaoka, Soichi Watanabe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicElectromagnetic Fields and Biological Effects
Canadian institutionsnot available
FundersNational Institute of Information and Communications TechnologyVector Institute
KeywordsSpecific absorption rateFrequency bandHomogeneousElectromagnetic fieldFinite-difference time-domain methodAbsorption (acoustics)Radio frequencyElectromagnetic radiationFrequency domainAcousticsVoxelRemote sensingComputer sciencePhysicsTelecommunicationsGeologyOpticsArtificial intelligenceStatistical physicsComputer vision

Abstract

fetched live from OpenAlex

Recently, there has been an increasing concern regarding the effects of electromagnetic waves on the health of humans. The safety of radio frequency electromagnetic fields (RF-EMFs) is evaluated by the specific absorption rate (SAR). In recent years, SAR has been estimated by numerical simulation using fine-resolution and anatomically realistic reference whole-body voxel models of people of various ages. The variation in SAR with a change in the physical features of a real person is hardly studied, although every person has different physical features. In this study, in order to estimate the individual variability in SAR of persons, we obtained considerable 3D body shape data from actual three-year-old children and developed several homogeneous models of these children. The variability in SAR of the homogeneous models of three-year-old children for whole-body exposure to RF electromagnetic fields in the very high frequency (VHF) band calculated using the finite-difference time-domain method has been described.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score0.193

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.214
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2009
Admission routes1
Has abstractyes

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