MétaCan
Menu
Back to cohort
Record W2288171240 · doi:10.1115/1.4031381

Fission Product Release Under Supercritical Water-Cooled Reactor Conditions

2016· article· en· W2288171240 on OpenAlexafffund
D. Guzonas, Linjing Qiu, Steve Livingstone, Stephane Rousseau

Bibliographic record

VenueJournal of Nuclear Engineering and Radiation Science · 2016
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsCanadian Nuclear Laboratories
FundersNatural Resources CanadaCanadian Nuclear Laboratories
KeywordsCoolantSupercritical fluidNuclear fission productBurnupFission productsNuclear engineeringFuel element failureMaterials sciencePelletsNuclear reactor corePelletLeaching (pedology)Pressurized water reactorFissionEnvironmental scienceNuclear physicsChemistryComposite materialNeutron

Abstract

fetched live from OpenAlex

Most supercritical water-cooled reactor (SCWR) concepts being considered as part of the Generation IV initiative are direct cycle. In the event of a fuel defect, the coolant will contact the fuel pellet, potentially releasing fission products and actinides into the coolant and transporting them to the turbines. At the high pressure (25 MPa) in an SCWR, the coolant does not undergo a phase change as it passes through the critical temperature in the core, and nongaseous species may be transported out of the core and deposited on out-of-core components, leading to increased worker dose. It is therefore important to identify species with a high risk of release and develop models of their transport and deposition behavior. This paper presents the results of preliminary leaching tests in SCW of U-Th simulated fuel pellets prepared from natural U and Th containing representative concentrations of the (inactive) oxides of fission products corresponding to a fuel burnup of 60 GWd/ton. The results show that Sr and Ba are released at relatively high concentrations at 400°C and 500°C.

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)0.001
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 score0.034
Threshold uncertainty score0.487

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.010
GPT teacher head0.228
Teacher spread0.218 · 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 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

Citations4
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Nuclear Engineering and Radiation ScienceSame topicNuclear Materials and PropertiesFrench-language works237,207