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Record W2168785030 · doi:10.1093/rpd/ncu366

EURADOS intercomparison on emergency radiobioassay

2014· article· en· W2168785030 on OpenAlexaff
Chunsheng Li, P. Battisti, P. Bérard, A. Cazoulat, Cuellar Antonio, R. Cruz-Suarez, X. Dai, Isabella Giardina, Derek Hammond, Carolina Hernández, Stephen Kiser, Raymond Ko, Sheila Kramer-Tremblay, Yannick Lecompte, E. Coll Navarro, Cristina Navas, Baki Sadi, I. Sierra, F. Verrezen, M. A. López

Bibliographic record

VenueRadiation Protection Dosimetry · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicRadioactive contamination and transfer
Canadian institutionsAtomic Energy (Canada)Health Canada
Fundersnot available
KeywordsEnvironmental science

Abstract

fetched live from OpenAlex

Nine laboratories participated in an intercomparison exercise organised by the European Radiation Dosimetry Group (EURADOS) for emergency radiobioassay involving four high-risk radionuclides ((239)Pu, (241)Am, (90)Sr and (226)Ra). Diverse methods of analysis were used by the participating laboratories for the in vitro determination of each of the four radionuclides in urine samples. Almost all the methods used are sensitive enough to meet the requirements for emergency radiobioassay derived for this project in reference to the Clinical Decision Guide introduced by the NCRP. Results from most of the methods meet the requirements of ISO 28218 on accuracy in terms of relative bias and relative precision. However, some technical gaps have been identified. For example, some laboratories do not have the ability to assay samples containing (226)Ra, and sample turnaround time would be expected to be much shorter than that reported by many laboratories, as timely results for internal contamination and early decisions on medical intervention are highly desired. Participating laboratories are expected to learn from each other on the methods used to improve the interoperability among these laboratories.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.917
Threshold uncertainty score0.998

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)0.0060.002

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 head0.010
GPT teacher head0.227
Teacher spread0.218 · 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; both teacher heads agree on what is shown here.

Study designOther design
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

Citations9
Published2014
Admission routes1
Has abstractyes

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