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Record W3037533344 · doi:10.3390/app10134488

Ce- and B-Doped Silica Fibers for Monitoring Low-Energy Proton Beams on a Medical Cyclotron

2020· article· en· W3037533344 on OpenAlexafffundabout
Cornelia Hoehr, Matthew Hanna, Stefan Zeisler, Crystal Penner, Matthew Hughes Stokely, Morgan Dehnel

Bibliographic record

VenueApplied Sciences · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaTRIUMF
FundersNatural Sciences and Engineering Research Council of CanadaTRIUMF
KeywordsCyclotronIrradiationBeam (structure)Materials scienceProtonDopantOpticsFiberDopingRadiochemistryNuclear physicsPhysicsOptoelectronicsChemistryPlasmaComposite material

Abstract

fetched live from OpenAlex

Many medical isotopes can be produced on a small cyclotron. The alignment and profiles of low-energy proton beams from cyclotrons used for medical radioisotope production, such as the TR13 cyclotron at TRIUMF, Canada, cannot be directly quantified during dose delivery with simultaneous constant feedback and sharp spatial resolutions. Doped silica fibers are a potential solution that has been tested at TRIUMF. To measure the effects of irradiation inside an isotope production target, we attached fibers to the outside of an 18O gas target and measured the light output during irradiation. Different dopants, fiber diameters, and target materials were investigated. It was found that 200 µm diameter Ce- and B-doped fibers produce signals linearly proportional to the beam current. This only deviated when the target was moved such that the beam was steered into the target wall, increasing the production of prompt radiation and causing the beam current to decrease but the fiber signal to increase. With the technique described here, the beam can be monitored on the target, including its steering and its overall alignment with the target.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.807
Threshold uncertainty score0.275

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 head0.260
Teacher spread0.242 · 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 designBench or experimental
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

Citations10
Published2020
Admission routes3
Has abstractyes

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