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Record W1983445471 · doi:10.1118/1.4740211

Sci—Sat AM: Brachy — 04: Neutron production around a radiation therapy linac bunker ‐ monte carlo simulations and physical measurements

2012· article· en· W1983445471 on OpenAlexaff
Rafael Khatchadourian, Stephen Davis, Michael D. Evans, A Licea, Jan Seuntjens, J. Kildea

Bibliographic record

VenueMedical Physics · 2012
Typearticle
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsCanadian Nuclear Safety CommissionMcGill University Health CentreMcGill University
Fundersnot available
KeywordsLinear particle acceleratorNeutronMonte Carlo methodPhysicsElectromagnetic shieldingBunkerNuclear physicsEquivalent doseNeutron sourceDosimetryNuclear engineeringNuclear medicineOpticsMedicineChemistryEngineeringMathematics

Abstract

fetched live from OpenAlex

Photoneutrons are a major component of the equivalent dose in the maze and near the door of linac bunkers. Physical measurements and Monte Carlo (MC) calculations of neutron dose are key for validating bunker design with respect to health regulations. We attempted to use bubble detectors and a 3He neutron spectrometer to measure neutron equivalent dose and neutron spectra in the maze and near the door of one of our bunkers. We also ran MC simulations with MCNP5 to measure the neutron fluence in the same region. Using a point source of neutrons, a Clinac 1800 linac operating at 10 MV was simulated and the fluence measured at various locations of interest. We describe the challenges faced when measuring dose with bubble detectors in the maze and the complexity of photoneutron spectrometry with linacs operating in pulsed mode. Finally, we report on the development of a userfriendly GUI for shielding calculations based on the NCRP 151 formalism.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.040
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0400.005

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 head0.046
GPT teacher head0.320
Teacher spread0.274 · 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 designSimulation or modeling
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

Citations0
Published2012
Admission routes1
Has abstractyes

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