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Record W4415952992 · doi:10.1051/epjconf/202533809012

Dosimetry with a phoswich detector

2025· article· en· W4415952992 on OpenAlexaff
Nicolás Ávila, D. D. Döhler, Pia Kahle, Anja Seifert, T. Kormoll

Bibliographic record

VenueEPJ Web of Conferences · 2025
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsDetectorMonte Carlo methodDosimetryPhotonCalibrationRadiationIonization chamberRange (aeronautics)

Abstract

fetched live from OpenAlex

A phoswich detector concept based on the combination of a high-Z element and a plastic element with a free running analogue-to-digital converter is presented in this work for the simultaneous measurement of operational dosimetric quantities H ∗ (10) and H ′ (0.07) in photon radiation fields. By means of a Pulse Shape Discrimination method and down to an energy of 60 keV ( 241 Am) in continuous photon radiation fields, the signal arriving at the common light sensor was separated into components coming from each element of the detector. In this context, crosstalk between the sensitive volumes represented an obstacle and was solved with a proper covering over one of the elements. Additionally, an algorithm for the calculation of the intended quantities based on the measured deposited doses was developed and tested using Monte Carlo methods with monoenergetic and X-ray radiation fields. Simulations set a validity region for the algorithm to the energy range between 20 keV and 3 MeV in the case of monoenergetic radiation and a lower energy cut of 15 keV in the case of X-ray fields. Furthermore, the algorithm was tested experimentally using 241 Am (60 keV) and 137 Cs (662 keV) sources against reference values from an OD-01 ionization chamber. Results showed a good sensitivity to the incoming photon energy, reflected in the variation of the ratio between the measured doses with the considered energies. At the same time, discrepancies between the measured and reference values of the operational quantities highlighted the need for a better calibration of the algorithm.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.478
Threshold uncertainty score0.475

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 head0.011
GPT teacher head0.276
Teacher spread0.265 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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