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Record W2874171772 · doi:10.1016/j.envint.2018.06.038

Occupational exposure to high-frequency electromagnetic fields and brain tumor risk in the INTEROCC study: An individualized assessment approach

2018· article· en· W2874171772 on OpenAlexafffund
Javier Vila, Michelle C. Turner, Esther Gràcia‐Lavedan, Jordi Figuerola, Joseph D. Bowman, Laurel Kincl, Lesley Richardson, Geza Benke, Martine Hours, Daniel Krewski, Dave McLean, Marie‐Elise Parent, Siegal Sadetzki, Klaus Schlaefer, Brigitte Schlehofer, Joachim Schüz, Jack Siemiatycki, Martie van Tongeren, Elisabeth Cardis

Bibliographic record

VenueEnvironment International · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicElectromagnetic Fields and Biological Effects
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité de MontréalUniversity of Ottawa
FundersDepartament de Salut, Generalitat de CatalunyaNational Cancer InstituteSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthWaikato Medical Research FoundationWellington Medical Research FoundationCancer Council NSWNational Health and Medical Research CouncilHealth and Safety ExecutiveCentres de Recerca de CatalunyaGeneralitat de CatalunyaNational Institute for Health and Care ResearchMedical Research CouncilCancer Research SocietyCancer Council VictoriaCancer Society of New ZealandAgence Française de Sécurité Sanitaire de l'Environnement et du TravailUniversity of OttawaWorld Health OrganizationEuropean CommissionCanadian Institutes of Health ResearchAssociation pour la Recherche sur le Cancer
KeywordsMedicineConfidence intervalOdds ratioPercentilePopulationBrain cancerLogistic regressionMeningiomaEpidemiologyCancerInternal medicineEnvironmental healthSurgeryStatisticsMathematics

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.506
Threshold uncertainty score0.402

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 head0.008
GPT teacher head0.265
Teacher spread0.258 · 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 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

Citations33
Published2018
Admission routes2
Has abstractno

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