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Record W2946821526 · doi:10.1097/hp.0000000000001099

Radon Off-Gassing From Military Artifacts

2019· article· en· W2946821526 on OpenAlexaff
David G. Kelly, Timothy Mumby

Bibliographic record

VenueHealth Physics · 2019
Typearticle
Languageen
FieldHealth Professions
TopicRadioactivity and Radon Measurements
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsEnvironmental scienceRadonGamma spectroscopySemiconductor detectorArtifact (error)RadiochemistryNuclear medicineDetectorPhysicsChemistryNuclear physicsOpticsBiologyMedicine

Abstract

fetched live from OpenAlex

Military historical artifacts found in museum displays and storage locations were analyzed for their Ra and Rn progeny activities to determine the fraction of Rn lost to the environment. Gamma-ray spectroscopy using high-purity germanium detectors was used to determine Ra activity and infer Rn activity based on Pb and Bi. Analyses were conducted without affecting the structural integrity of the artifacts. Ra was measured directly after correction for solid angle and finite sample-detector distance. Although Rn can be similarly analyzed, the collection in charcoal of Rn off-gassed from the artifact after the establishment of secular equilibrium was preferable. Rn off-gassing rates vary greatly between the six devices studied, with a maximum off-gassing rate of 1,850 ± 50 Bq h. Large variations in off-gassing rate were also observed between an additional 30 nominally identical dials, with a mean and standard deviation of 7.7 ± 7.1 Bq h. The work is not predictive of airborne Rn activity within museums, where building size and ventilation are significant and unique to each location. However, the significant off-gassing rates and their large variation suggest that Rn activities may be elevated in enclosed locations, such as aircraft cockpits and storage facilities.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.374
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.004

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 head0.112
GPT teacher head0.422
Teacher spread0.310 · 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.

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
Published2019
Admission routes1
Has abstractyes

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