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Record W3004379912 · doi:10.1093/rpd/ncz295

ELECTRON EYE-LENS OPERATIONAL DOSE COEFFICIENTS

2019· article· en· W3004379912 on OpenAlexafffund
J. Dubeau, Jiancheng Sun

Bibliographic record

VenueRadiation Protection Dosimetry · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsCanadian Nuclear Laboratories
FundersCANDU Owners Group
KeywordsEye lensDosimeterLens (geology)ElectronPhotonRadiation protectionEquivalent doseFluencePhysicsDose rateIonizing radiationRadiationOpticsMaterials scienceEnvironmental scienceComputational physicsNuclear physicsIrradiationMedical physics

Abstract

fetched live from OpenAlex

In 2012, the International Commission on Radiological Protection issued a recommendation for a reduced annual eye-lens dose limit in the face of mounting evidence of the risk of cataract induction. This led to worldwide research efforts in various areas including the dose simulation in realistic eye-models, the production of dosimeters and the elaboration of protection and operation fluence to eye-lens dose coefficients. In this last case, much efforts have been expanded with regards to photon operational coefficients for Hp (3) but much less for electron radiation. In this work, Hp (3) coefficients for electrons are presented following simulations using MCNP and compared to those that are available in the literature. It is found that, at energies of 1 MeV and less, Hp (3) coefficients depend strongly on the selected electron transport options and on the dose tally volume. The effect of these differences is demonstrated for two beta emitters.

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 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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.008
GPT teacher head0.241
Teacher spread0.233 · 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 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

Citations7
Published2019
Admission routes2
Has abstractyes

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