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Record W1022172678 · doi:10.13182/nt11-a13320

Laboratory Tests of an Ultrasonic Inspection Technique to Identify Defective CANDU Fuel Elements

2011· article· en· W1022172678 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.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueNuclear Technology · 2011
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsRoyal Military College of Canada
FundersNatural Sciences and Engineering Research Council of CanadaUniversity Network of Excellence in Nuclear Engineering
KeywordsNuclear engineeringPelletsCoolantNuclear fuelUltrasonic sensorBundleUltrasonic testingSpent fuel poolUraniumSpent nuclear fuelMaterials scienceFuel element failureEnvironmental scienceMechanical engineeringEngineeringAcousticsPhysicsComposite materialMetallurgy

Abstract

fetched live from OpenAlex

In the rare occurrence of a fuel failure during normal operation, the primary coolant can enter the element. Visual techniques are normally used for the postirradiation inspection of discharged CANadian Deuterium Uranium (CANDU) fuel bundles to help identify such failures. In this work, a more sensitive method, based on underwater angled-beam ultrasonic inspection, is investigated under laboratory conditions. Only nonirradiated fuel elements were tested. Identification is possible with the introduction of water into the fuel element, which acts as a couplant for sound waves, thereby providing for a clear demarcation of the fuel pellets within the element in observed scans. This study therefore demonstrates that the inspection of the outer-ring (i.e., higher-powered) elements in the complex fuel bundle structure is possible.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.978

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)0.0010.000

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 head0.018
GPT teacher head0.265
Teacher spread0.247 · 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