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Record W1969751059 · doi:10.1118/1.1386423

A two‐source model for electron beams: Calculation of relative output factors

2001· article· en· W1969751059 on OpenAlexafffund
Jiaoyang Chen, Jake VanDyk, C. Lewis, Jerry Battista

Bibliographic record

VenueMedical Physics · 2001
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsWestern University
FundersCancer Care Ontario
KeywordsInverse-square lawAperture (computer memory)PhysicsOpticsSquare (algebra)InverseElectron scatteringComputational physicsScatteringElectronCathode rayField (mathematics)Monte Carlo methodBeam (structure)Range (aeronautics)Square rootMathematicsGeometryMaterials scienceQuantum mechanicsAcoustics

Abstract

fetched live from OpenAlex

A two-source model for the calculation of relative output factors (ROF) for clinical applications of electron beams has been developed. The model consists of (1) an effective extended source above the final field-defining aperture (cutout) plane and (2) a source due to scattering from the aperture. Calculations are based on Fermi-Eyges theory and a pencil beam algorithm with parameters determined independently for each major scattering component. The model predicts a modified inverse square law for determining the dose rate for the electron beams. It also generalizes the "square-root method" and "one-dimensional method" that are often used clinically for ROF calculations. A computer program based on the model has been developed to calculate ROF for irregular fields. The predictions of ROF values have been compared with measurements on a Varian CLINAC 2100C/D accelerator for different cutout size, energies, applicators, and SSDs for square fields, rectangular fields, circular fields, and irregular fields. The agreement between prediction and measurement of the ROF for these wide range of conditions is generally within 1% for energies from 6 to 20 MeV. This two-source model can be used for clinical applications and it requires a minimal set of measured input data.

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: none
Teacher disagreement score0.901
Threshold uncertainty score0.472

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 head0.018
GPT teacher head0.308
Teacher spread0.291 · 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

Citations25
Published2001
Admission routes2
Has abstractyes

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