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Record W2035704449 · doi:10.1118/1.1803811

Accuracy of the Burns equation for stopping‐power ratio as a function of depth and

2004· letter· en· W2035704449 on OpenAlexaff
D. W. O. Rogers

Bibliographic record

VenueMedical Physics · 2004
Typeletter
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsCarleton University
Fundersnot available
KeywordsStopping powerImaging phantomPhysicsBeam (structure)Power (physics)DosimetryCathode rayFunction (biology)OpticsNuclear medicineElectronComputational physicsAtomic physicsNuclear physicsDetectorMedicine

Abstract

fetched live from OpenAlex

The accuracy of the Burns et al. equation [Med. Phys. 23, 489-501 (1996)] for the Spencer-Attix water to air stopping-power ratio as a function of depth in a water phantom and electron beam quality in terms of R50 is investigated by comparison to the original data on which this fit was based. It is shown that using this equation provides dose estimates on the central axis in a clinical electron beam that are accurate to within 1% of dose maximum for all 24 clinical beams investigated except very close to the surface in swept beams. In contrast, the error in the dose as a percentage of the local dose is much higher for values of the depth/R50 greater than 1.2.

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)0.010
metaresearch head score (Gemma)0.067
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: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.067
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0020.003

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 head0.293
Teacher spread0.276 · 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
GenreCommentary

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

Citations9
Published2004
Admission routes1
Has abstractyes

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