MétaCan
Menu
Back to cohort
Record W2170204919 · doi:10.1088/0031-9155/53/8/n01

Validation of a Monte Carlo model of a NACP-02 plane-parallel ionization chamber model using electron backscatter experiments

2008· article· en· W2170204919 on OpenAlexaff
E Chin, D R Shipley, Michael Bailey, Jan Seuntjens, Hugo Palmans, A R DuSautoy, Frank Verhaegen

Bibliographic record

VenuePhysics in Medicine and Biology · 2008
Typearticle
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsMcGill UniversityMontreal General Hospital
FundersNational Cancer Institute
KeywordsMonte Carlo methodBackscatter (email)ElectronIonizationComputational physicsIonization chamberStatistical physicsPhysicsNuclear physicsComputer scienceIonStatisticsMathematics

Abstract

fetched live from OpenAlex

The accuracy of Monte Carlo (MC) simulation results relies on validating the MC models used in the calculations. In this work, a MC model for the NACP-02 plane-parallel ionization chamber was built and validated against megavoltage electron backscatter experiments using materials of water, graphite, aluminium and copper. Electron energies ranged between 6-18 MeV and the chamber's air cavity was at the depth of maximum dose, z(max). A chamber model based on manufacturer's specifications resulted in systematic discrepancies of several percents between measured and simulated backscatter factors. Tuning of the MC chamber model against backscatter factors to improve agreement increased the chamber's front window mass thickness by 35% over the reported value of 104 mg cm(-2) in the IAEA's TRS-398 absorbed dose protocol. The large increase in chamber window mass thickness was verified by measurements on a disassembled NACP-02 chamber. The new backscatter factor results based on the tuned MC NACP-02 chamber model matched the experimental results within 1-2 standard deviations. We conclude therefore that for MC simulations near z(max), tuning of the NACP-02 chamber model against experimental backscatter measurements is an acceptable method for validating the chamber model.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.780
Threshold uncertainty score0.309

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 head0.225
GPT teacher head0.393
Teacher spread0.168 · 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 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

Citations19
Published2008
Admission routes1
Has abstractyes

Explore more

Same venuePhysics in Medicine and BiologySame topicRadiation Therapy and DosimetryFrench-language works237,207