MétaCan
Menu
Back to cohort
Record W236234621 · doi:10.1520/stp154320120157

Modeling Irradiation Damage in Zr-2.5Nb and Its Effects on Delayed Hydride Cracking Growth Rate

2014· book-chapter· en· W236234621 on OpenAlexaff
Grant A. Bickel, M. Griffiths, Heather Chaput, A.G. Buyers, C.E. Coleman

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsAtomic Energy (Canada)Canadian Nuclear Laboratories
Fundersnot available
KeywordsHydrideCrackingIrradiationMaterials scienceDose rateChemistryMetallurgyRadiochemistryComposite materialNuclear physicsPhysics

Abstract

fetched live from OpenAlex

Zr-2.5Nb is a dual-phase alloy consisting of an hcp (α) phase containing up to 1 wt. % Nb and a bcc (β) phase containing about 20 wt. % Nb. The α phase constitutes the majority of the material volume. For in-service Zr-2.5Nb CANDU pressure tubes, the structures of both the α and β phases evolve as a result of the effects of irradiation and operating temperature: dislocation loop formation in the α phase and decomposition or reconstitution of the β phase. X-ray diffraction data are used to study the irradiation damage (represented by the integral breadth of hcp diffraction peaks and the lattice parameter of the β phase). This evolution of the microstructure must be modeled as a function of operating conditions so that the state of the microstructure of in-service pressure tubes can be predicted. Delayed hydride cracking (DHC) growth rates in Zr-2.5Nb CANDU pressure tube material also depends on the state of the microstructure. In this paper, it is shown that the majority of the DHC growth rate changes can be ascribed to thermal and irradiation effects on the microstructure.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.213
Teacher spread0.195 · 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 designSimulation or modeling
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

Citations9
Published2014
Admission routes1
Has abstractyes

Explore more

Same topicNuclear Materials and PropertiesFrench-language works237,207