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Record W1969327310 · doi:10.1088/0031-9155/49/16/019

A small-body portable graphite calorimeter for dosimetry in low-energy clinical proton beams

2004· article· en· W1969327310 on OpenAlexaff
Hugo Palmans, Russell Thomas, Mary Simon, S Duane, Andrzej Kacperek, A R DuSautoy, Frank Verhaegen

Bibliographic record

VenuePhysics in Medicine and Biology · 2004
Typearticle
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsMcGill University
FundersNational Physical Laboratory
KeywordsDosimetryCalorimeter (particle physics)GraphiteProtonMaterials scienceNuclear engineeringNuclear physicsMedical physicsNuclear medicinePhysicsOpticsMedicineDetectorEngineeringComposite material

Abstract

fetched live from OpenAlex

Calorimetry has been recommended and performed in proton beams for some time, but never has graphite calorimetry been used as a reference dosimeter in clinical proton beams. Furthermore, only a few calorimetry measurements have been reported in ocular proton beams. In this paper we describe the construction and performance of a small-body portable graphite calorimeter for clinical low-energy proton beams. Perturbation correction factors for the gap effect, volume averaging effect, heat transfer phenomena and impurity effect are calculated and applied in a comparison with ionization chamber dosimetry following IAEA TRS-398. The ratio of absorbed dose to water obtained from the calorimeter measurements and from the ionization measurements varied between 0.983 and 1.019, depending on the beam type and the ionization chamber calibration modality. Standard uncertainties on these values varied between 1.9% and 2.5% including a substantial contribution from the kQ values in IAEA TRS-398. The (Wair/e)p values inferred from these measurements varied between 33.6 J C(-1) and 34.9 J C(-1) with similar standard uncertainties. A number of improvements for the small-body portable graphite calorimeter and the experimental set-up are suggested for potential reduction of the uncertainties.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.527
Threshold uncertainty score0.468

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.188
GPT teacher head0.422
Teacher spread0.234 · 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

Citations70
Published2004
Admission routes1
Has abstractyes

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