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Record W4224128246 · doi:10.1016/j.envint.2022.107189

Estimation of RF and ELF dose by anatomical location in the brain from wireless phones in the MOBI-Kids study

2022· article· en· W4224128246 on OpenAlexfundno aff
Carolina Calderón, Gemma Castaño‐Vinyals, Myron Maslanyj, Joe Wiart, Ae‐Kyoung Lee, Masao Taki, Kanako Wake, Alex Abert, Francesc Badia, Abdelhamid Hadjem, Hans Kromhout, Patricia de Llobet, Nadège Varsier, Emmanuelle Conil, Hyung‐Do Choi, Malcolm Sim, Elisabeth Cardis

Bibliographic record

VenueEnvironment International · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicElectromagnetic Fields and Biological Effects
Canadian institutionsnot available
FundersBoard of Research in Nuclear SciencesHealth Research Council of New ZealandMinisterio de Ciencia, Innovación y UniversidadesConsejería de Salud y Familias, Junta de AndalucíaMinistero della SaluteZonMwMinistry of Internal Affairs and CommunicationsCanadian Institutes of Health ResearchCure KidsGeneralitat de CatalunyaMinistry of Science and ICT, South KoreaEuropean CommissionBundesamt für Strahlenschutz
KeywordsMobile phoneGSMRadio frequencyWirelessMedicineTelecommunicationsAudiologyComputer science

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.223
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations11
Published2022
Admission routes1
Has abstractyes

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