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Record W2089054720 · doi:10.1118/1.2030993

Po‐Poster ‐ 14: In‐house leak testing with a multi‐channel analyzer system

2005· article· en· W2089054720 on OpenAlexaffabout
W Abdel‐Rahman, Robert Corns, Michael D. Evans, E. B. Podgoršak

Bibliographic record

VenueMedical Physics · 2005
Typearticle
Languageen
FieldEngineering
TopicNuclear and radioactivity studies
Canadian institutionsThames Valley Children's CentreMcGill University
Fundersnot available
KeywordsLeakSpectrum analyzerNuclear medicinePhysicsEnvironmental scienceEngineeringMedicineElectrical engineering

Abstract

fetched live from OpenAlex

The URSA‐II Multi‐Channel Analyzer (MCA) (URSA‐II Radiation Safety Associates, Inc. Hebron, CT) connected to a NaI well counter (PR‐4112, Ludlum Measurements, Inc. Sweetwater, TX) is an ideal instrument for performing accurate assays of samples with low levels of activity. Clinically this is important for assaying leak‐test samples taken from sealed sources. Leak testing is a Canadian Nuclear Safety Commission (CNSC) licensing requirement for all sealed sources with activities exceeding 50 MBq. The URSA‐II was evaluated by calibrating the system using a set of known Co‐60 and Cs‐137 sources and then assaying a second set of known Co‐60 and Cs‐137 test sources. The measured activities for the test sources set were within ±5% of the values listed on the certificate once corrected for decay. We found the minimum detectable activity (MDA) for this instrument is 7 Bq for Co‐60 and 3 Bq for Cs‐137 when using a fixed counting time of 60 seconds. These MDAs demonstrate this instrument would have no difficulties in determining if a leak test sample is under the 200 Bq action level as dictated by the CNSC in the CNSC regulatory document R‐116 for leak testing. The URSA‐II is a convenient and simple tool for performing in‐house leak testing as part of a standard license compliance program for class II clinical installations.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score0.445

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 head0.019
GPT teacher head0.220
Teacher spread0.201 · 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

Citations0
Published2005
Admission routes2
Has abstractyes

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