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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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.004

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 head0.024
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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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