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Record W330788345 · doi:10.13182/nt13-a15824

Viability Assessment of Noble Gas Bundle Tagging for Failed-Fuel Identification in CANDU Reactors

2013· article· en· W330788345 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueNuclear Technology · 2013
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsNuclear engineeringNoble gasMass spectrometryKryptonDeuteriumNuclear fuelChemistryEnriched uraniumNuclear reactorUraniumNuclear physicsXenonPhysicsChromatographyEngineering

Abstract

fetched live from OpenAlex

Current limitations of Canadian Deuterium Uranium (CANDU) reactors to reliably locate defective fuel bundles have created interest in new identification techniques. Noble gas tagging, which would involve the addition of specific combinations of Kr and Xe isotopes to the fuel-to-sheath gap during manufacturing, has the potential to offer a means of locating failed-fuel bundles on power, where the released tag could be measured in the primary heat transport system by mass spectrometry. Moreover, the technique could be of particular interest for demonstration irradiations with new fuel bundle designs. This work outlines preliminary considerations on the applicability of noble gas tagging for CANDU reactors. This assessment involved the determination of suitable tag isotopes, the simulation of the impact of the tag on the thermal performance of a fuel element, and the determination of the detection limit of a quadrupole inductively coupled plasma-mass spectrometer instrument for krypton samples with typical aqueous concentrations in the range of 10-12 to 10-9 (molKr/molH2O).

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.895
Threshold uncertainty score0.516

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 head0.005
GPT teacher head0.213
Teacher spread0.207 · 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