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Record W2331230998 · doi:10.1093/rpd/nct260

Intercomparison of Monte Carlo radiation transport codes to model TEPC response in low-energy neutron and gamma-ray fields

2013· article· en· W2331230998 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRadiation Protection Dosimetry · 2013
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsUniversity of Ontario Institute of Technology
Fundersnot available
KeywordsMonte Carlo methodPhysicsNeutronBenchmark (surveying)Gamma rayRadiationComputational physicsNuclear engineeringNuclear physicsStatisticsMathematics

Abstract

fetched live from OpenAlex

Tissue-equivalent proportional counters (TEPC) can potentially be used as a portable and personal dosemeter in mixed neutron and gamma-ray fields, but what hinders this use is their typically large physical size. To formulate compact TEPC designs, the use of a Monte Carlo transport code is necessary to predict the performance of compact designs in these fields. To perform this modelling, three candidate codes were assessed: MCNPX 2.7.E, FLUKA 2011.2 and PHITS 2.24. In each code, benchmark simulations were performed involving the irradiation of a 5-in. TEPC with monoenergetic neutron fields and a 4-in. wall-less TEPC with monoenergetic gamma-ray fields. The frequency and dose mean lineal energies and dose distributions calculated from each code were compared with experimentally determined data. For the neutron benchmark simulations, PHITS produces data closest to the experimental values and for the gamma-ray benchmark simulations, FLUKA yields data closest to the experimentally determined quantities.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.599

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 head0.006
GPT teacher head0.192
Teacher spread0.186 · 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