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Record W2137537652 · doi:10.1093/rpd/ncv165

Environmental microdosimetry: microdosimetric characterisation of low-dose exposures

2015· article· en· W2137537652 on OpenAlexafffund
A.J. Waker, T. Mahilrajan, H. Sandhu

Bibliographic record

VenueRadiation Protection Dosimetry · 2015
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of CanadaUniversity Network of Excellence in Nuclear EngineeringUniversity of Ontario Institute of Technology
KeywordsDosimetryRadiobiologyMonte Carlo methodIonizing radiationNuclear medicineInternal dosimetryAbsorbed dosePhysicsMedical physicsRadiochemistryComputer scienceNuclear physicsMedicineChemistryIrradiationMathematicsStatistics

Abstract

fetched live from OpenAlex

A number of researchers, as well as the International Commission on Radiation Units and Measurements, have described how concepts and quantities used in microdosimetry best capture the stochastic nature of low-level exposures in terms of cell hits and the fraction of cells affected within a tissue. However, the concepts of microdosimetry are not generally intuitive to the public or indeed to health physicists. In this article, the methods of conventional internal dosimetry was applied to different forms of radioactive iodine to derive cell-hit numbers and cell fractions affected by low-level exposures, and it is shown that microdosimetric analysis is compatible with conventional dosimetry but has the advantage of underscoring the stochastic nature of ionising radiation at low dose. The microdosimetric description of low-dose exposures derived in this work could be improved with the use of Monte Carlo track structure codes and more realistic models of different tissues and their cellular structure.

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)0.001
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 score0.292
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.022
GPT teacher head0.251
Teacher spread0.229 · 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

Citations1
Published2015
Admission routes2
Has abstractyes

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