MétaCan
Menu
Back to cohort
Record W2079613981 · doi:10.1118/1.2219331

Efficient x-ray tube simulations

2006· article· en· W2079613981 on OpenAlexaff
Ernesto Mainegra‐Hing, I. Kawrakow

Bibliographic record

VenueMedical Physics · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsBremsstrahlungKermaMonte Carlo methodRADIUSPhysicsPhotonComputational physicsX-ray tubeHalf-value layerElectronDosimetryTube (container)OpticsMaterials scienceNuclear physicsMean free pathNuclear medicineComputer scienceMathematicsStatistics

Abstract

fetched live from OpenAlex

This article describes an efficiency study of directional bremsstrahlung splitting (DBS) for x-ray tube modeling. DBS is shown to be up to five or six orders of magnitude more efficient at 50 or 135 kV tube potential than a simulation without splitting, and 60 times more efficient compared to uniform bremsstrahlung splitting. A methodology is presented to determine the optimum splitting number for a given situation using a second degree polynomial expression derived from theoretical considerations. Very large optimum splitting numbers are found for small fields (5 mm radius) at 1 m from the x-ray source, which are relevant for half-value layer (HVL) calculations and for simulations related to primary air kerma standards. Two approaches for the calculation of kerma at a plane and inside a volume using track-length estimation are implemented in BEAMnrc, a user-code from the EGSnrc Monte Carlo simulation system for photon and electron transport. A practical application of DBS to HVL calculations for a Comet MXR-320 x-ray tube is reported. The agreement with measured HVLs at different constant tube potentials is found to be better than 2.3%.

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 score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.007
GPT teacher head0.276
Teacher spread0.269 · 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

Citations92
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueMedical PhysicsSame topicAdvanced Radiotherapy TechniquesFrench-language works237,207