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Record W2904624331 · doi:10.5006/c2018-10530

The Nuclear Energy Agency Contribution to Nuclear Materials Performance Knowledge Preservation

2018· article· en· W2904624331 on OpenAlexaff
Bengt Lydell, Olli Nevander, Jovica Riznic

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsCanadian Nuclear Safety Commission
Fundersnot available
KeywordsAgency (philosophy)Nuclear engineeringEnergy (signal processing)Materials scienceComputer scienceForensic engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract The goal of the Nuclear Energy Agency (NEA) (https://www.oecd-nea.org/) in the area of nuclear safety and regulation is to assist its member countries in ensuring high standards of safety in the use of nuclear energy, by supporting the development of effective and efficient regulation and oversight of nuclear installations, and by helping to maintain and advance the scientific and technological knowledge base. The Agency's joint projects and information exchange programs enable interested countries, on a cost-sharing basis, to pursue research and the sharing of data with respect to particular areas or problems. The projects are carried out under the auspices, and with the support, of the NEA. Such projects, primarily in the areas of nuclear safety and waste management, are one of the NEA's major strengths. Since 2002, the NEA operates an international database on materials performance issues. The "Component Operational Experience, Degradation & Ageing Programme" (CODAP) has been established to encourage multilateral co-operation in the collection and analysis of data relating to degradation and failure of metallic piping and non-piping metallic passive components in commercial nuclear power plants. The Project is organised under the OECD/NEA Committee on the Safety of Nuclear Installations (CSNI).

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.895
Threshold uncertainty score0.854

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)0.0000.001

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 head0.007
GPT teacher head0.187
Teacher spread0.181 · 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 designNot applicable
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

Citations0
Published2018
Admission routes1
Has abstractyes

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