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Record W2090074593 · doi:10.1088/0031-9155/57/2/437

Analytical modeling of relative luminescence efficiency of Al<sub>2</sub>O<sub>3</sub>:C optically stimulated luminescence detectors exposed to high-energy heavy charged particles

2011· article· en· W2090074593 on OpenAlexafffund
Gabriel O. Sawakuchi, E.G. Yukihara

Bibliographic record

VenuePhysics in Medicine and Biology · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLuminescenceOptically stimulated luminescenceDosimetryCharged particleLinear energy transferMaterials scienceAtomic physicsComputational physicsPhysicsOpticsIonIrradiationNuclear physicsNuclear medicine

Abstract

fetched live from OpenAlex

The objective of this work is to test analytical models to calculate the luminescence efficiency of Al(2)O(3):C optically stimulated luminescence detectors (OSLDs) exposed to heavy charged particles with energies relevant to space dosimetry and particle therapy. We used the track structure model to obtain an analytical expression for the relative luminescence efficiency based on the average radial dose distribution produced by the heavy charged particle. We compared the relative luminescence efficiency calculated using seven different radial dose distribution models, including a modified model introduced in this work, with experimental data. The results obtained using the modified radial dose distribution function agreed within 20% with experimental data from Al(2)O(3):C OSLDs relative luminescence efficiency for particles with atomic number ranging from 1 to 54 and linear energy transfer in water from 0.2 up to 1368 keV µm(-1). In spite of the significant improvement over other radial dose distribution models, understanding of the underlying physical processes associated with these radial dose distribution models remain elusive and may represent a limitation of the track structure model.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.105
GPT teacher head0.304
Teacher spread0.199 · 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

Citations12
Published2011
Admission routes2
Has abstractyes

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