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Record W2318656510 · doi:10.1097/hp.0000000000000145

Using Tea as an Artificial Urine in a Canadian Performance Testing Program for Fission/Activation Products

2014· article· en· W2318656510 on OpenAlexaffabout
Joseph N. Daka, G. Moodie, Anthony DiNardo, Gary H. Kramer

Bibliographic record

VenueHealth Physics · 2014
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsHealth Canada
Fundersnot available
KeywordsChemistryUrineRadiochemistryMethanolAlcoholMatrix (chemical analysis)Nuclear chemistryChromatographyHigh-performance liquid chromatographyBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

In recent years, the National Calibration Reference Centre for Bioassay and In Vivo Monitoring (NCRC) at the Radiation Protection Bureau (RPB), Health Canada, has been conducting investigations with black tea to develop a matrix that can be used to replace urine in each of the following performance testing programs (PTP): (1) tritium, (2) carbon-14, (3) the DUAL (i.e., 3H/14C), and (4) fission/activation products (F/AP). A 1% tea solution with thimerosal, which had worked successfully for tritium, carbon-14, and the DUAL, was selected and tested for the F/AP PTP because of its similarity to urine in color and UV-VIS spectra. However, application of this tea to samples of the F/AP program containing 133Ba, 137Cs, 57Co, and 60Co produced precipitates, which was an unexpected result. Further experiments showed that replacement of thimerosal with an alcohol at about 5% eliminated the precipitation problem. The alcohol can be ethanol, methanol, or isopropanol. In the experiments, the 1% tea, preserved with alcohol, remained clear and stable for at least 100 d. The duration of each PTP for the NCRC is limited to 90 d. Application of the CNSC S-106 regulatory standard to the tea produced acceptable accuracy and precision results. It was concluded that a suitable tea matrix for the F/AP program had been found.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.951
Threshold uncertainty score0.967

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 head0.121
GPT teacher head0.406
Teacher spread0.285 · 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 designOther design
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
Published2014
Admission routes2
Has abstractyes

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