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Record W2035833348 · doi:10.1088/0031-9155/53/6/002

Benchmarking EGSnrc in the kilovoltage energy range against experimental measurements of charged particle backscatter coefficients

2008· article· en· W2035833348 on OpenAlexaff
Elsayed Ali, D. W. O. Rogers

Bibliographic record

VenuePhysics in Medicine and Biology · 2008
Typearticle
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsCarleton University
Fundersnot available
KeywordsBackscatter (email)Range (aeronautics)PhysicsMonte Carlo methodComputational physicsBenchmark (surveying)Energy (signal processing)Computer scienceStatisticsMathematicsMaterials science

Abstract

fetched live from OpenAlex

This study benchmarks the EGSnrc Monte Carlo code in the energy range of interest to kilovoltage medical physics applications (5-140 keV) against experimental measurements of charged particle backscatter coefficients. The benchmark consists of experimental data from 20 different published experiments (1954-2007) covering 35 different elements (4<or=Z<or=92), electron and positron backscatter, normal and oblique incidence, and backscatter from thin films. EGSnrc simulation results show excellent agreement with the vast majority of the experimental data. Possible experimental and computational uncertainties explaining the few noted discrepancies are discussed. This study concludes that for the energy range of interest to kilovoltage medical physics application, EGSnrc produces backscatter results within approximately 4% of the average of the majority of published experimental data. A documented EGSnrc user-code customized for backscatter calculations is available from the authors at http://www.physics.carleton.ca/clrp/backscatter.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.240

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 head0.264
GPT teacher head0.382
Teacher spread0.118 · 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 designObservational
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

Citations57
Published2008
Admission routes1
Has abstractyes

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