The Nuclear Energy Agency Contribution to Nuclear Materials Performance Knowledge Preservation
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.015 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".