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Optical fibre array detector to monitor irradiations for medical radioisotope production

2022· article· en· W4311171748 on OpenAlexaff
Samuel Usherovich, Crystal Penner, Geoff Hodgson, Edward Thoeng, Morgan Dehnel, Cornelia Hoehr

Bibliographic record

VenueJournal of Physics Conference Series · 2022
Typearticle
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaUniversity of WaterlooTRIUMF
Fundersnot available
KeywordsCyclotronIrradiationMaterials scienceBeam (structure)Ionizing radiationDetectorRadiationOpticsOptical fiberOptoelectronicsRadiochemistryNuclear physicsPhysicsChemistry

Abstract

fetched live from OpenAlex

Low-energy cyclotrons are in use worldwide to produce medical radioisotopes for nuclear medicine. Beam monitoring during the irradiation of targets is difficult due to the high-power density of low-energy protons, space limitations, and interference with beam delivery. Doped silica fibres are sensitive to prompt ionizing radiation from the bombarded target, and produce radiation induced luminescence (RIL) when exposed. The fibres can be attached to the outside of the target in a low-profile fibre array, ensuring efficient and safe operation. Here, we present the results from our prototype of such a fibre array. It consists of four Ce-doped fibres with a diameter of 200 μm and has been installed at the TR13 medical cyclotron at TRIUMF where it has been tested at different irradiation conditions.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.002

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 head0.021
GPT teacher head0.291
Teacher spread0.270 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

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