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Record W2131466051 · doi:10.1115/icone18-30252

Rapid Bioassay Methods for Actinides in Urine

2010· article· en· W2131466051 on OpenAlexafffund
X. Dai, Sheila Kramer-Tremblay

Bibliographic record

Venue18th International Conference on Nuclear Engineering: Volume 3 · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicRadioactive contamination and transfer
Canadian institutionsAtomic Energy (Canada)
FundersAtomic Energy of Canada Limited
KeywordsActinideBioassayInductively coupled plasma mass spectrometryMass spectrometryRadiochemistryChemistryChromatographyUraniumNuclear chemistryMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

Rapid bioassay methods for the determination of actinides in urine samples at ultra-trace levels are needed for both emergency and routine radiation exposure monitoring. Several rapid actinide urinalysis methods have been recently developed at the AECL Chalk River Laboratories. These methods employ hydrous titanium oxide co-precipitation followed with actinide separation using chromatographic columns; the actinide isotopes are analyzed by alpha spectrometry and inductively coupled plasma mass spectrometry. The chemical recoveries, procedural blanks, and achieved detection limits for these bioassay methods are also presented.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score0.986

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)0.0150.000

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 head0.024
GPT teacher head0.297
Teacher spread0.273 · 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 designNot applicable
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

Citations1
Published2010
Admission routes2
Has abstractyes

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