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Record W3001902340 · doi:10.1080/16878507.2019.1711341

GEANT4 simulations of cosmic muon background in CEMRC BEGe lung detectors and detection sensitivity optimization of trans-uranic radionuclides

2020· article· en· W3001902340 on OpenAlexaff
J. Turko, I. Pillalamarri, P. Jagam

Bibliographic record

VenueJournal of Radiation Research and Applied Sciences · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPhysicsSemiconductor detectorNuclear physicsDetectorMuonGermaniumCosmic rayScintillatorSensitivity (control systems)CoincidenceRadionuclideDosimetryBackground radiationRadiochemistryOpticsNuclear medicineRadiationChemistryOptoelectronicsSilicon

Abstract

fetched live from OpenAlex

In this work, we discuss two results from GEANT4 simulations of the broad-energy germanium (BEGe) lung counting system used by the Carlsbad Environmental Monitoring and Research Center (CEMRC) internal dosimetry laboratory. The first component of the presented research consists of the integration of the cosmic muon background into the environmental background spectra and updated calculations of the minimum-detectable activities (MDAs) that were previously published by the authors. This is followed by an investigation of methods to increase the detection sensitivity to low-energy γ rays produced by the trans-uranic radioisotopes of interest by reducing the number of background counts observed in the region-of-interest (ROI) of the measured γ ray spectra measured by the CEMRC in vivo counting system. Two methods were investigated for improving the MDAs: (1) a reduction in the thickness of the germanium detector crystals, and (2) the addition of a Compton-suppression detection system surrounding the germanium detectors, acting as a anti-coincidence veto counter.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.257

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.049
GPT teacher head0.320
Teacher spread0.272 · 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 designSimulation or modeling
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

Citations6
Published2020
Admission routes1
Has abstractyes

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