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Record W2787883830 · doi:10.1109/nssmic.2017.8532703

Potential of Novel Optical Fibers for Proton Therapy Dosimetry

2017· article· en· W2787883830 on OpenAlexaffabout
Cornelia Hoehr, Adriana Morana, Olivier Duhamel, Bruno Capoen, M. Trinczek, Cheryl Duzenli, Philippe Paillet, Hicham El Hamzaoui, Géraud Bouwmans, Y. Ouerdane, A. Boukenter, Sylvain Girard

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Optical Sensing Technologies
Canadian institutionsBC Cancer AgencyTRIUMF
Fundersnot available
KeywordsProton therapyOptical fiberProtonDosimetryMaterials scienceOptoelectronicsOpticsPhysicsMedicineNuclear medicineNuclear physics

Abstract

fetched live from OpenAlex

We investigate the potential of innovative optical fiber bulk materials made by the sol-gel technique for proton therapy dosimetry. These types of glass are made of amorphous silica (a-SiO2) doped with either Copper (Cu) or Cerium (Ce) ions. All these optimized materials possess very interesting light emission properties when exposed to protons. Online measurement of the strong radiation-induced luminescence allows the monitoring of the time evolution of the proton flux with millisecond resolution and the cumulated proton fluence can be precisely deduced by integrating this radiation-induced luminescence signal. Preliminary tests presented here have been performed at the TRIUMF Proton Therapy facility in Vancouver, Canada, where ocular cancer is treated clinically with 74 MeV protons and a proton current of 6 nA.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.313
Teacher spread0.287 · 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

Citations7
Published2017
Admission routes2
Has abstractyes

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