SARNET - Severe Accident Research Network of Excellence
Bibliographic record
Abstract
Fifty-one organizations network in SARNET (Severe Accident Research NETwork of Excellence) their capacities of research in order to resolve the most important pending issues for enhancing, in regard of Severe Accidents (SA), the safety of existing and future\nNuclear Power Plants (NPPs). This project, co-funded by the European Commission (EC) under the 6th Framework Programme, has been defined in order to optimise the use of the available means and to constitute sustainable research groups in the European Union.\nSARNET tackles the fragmentation that may exist between the different national R&D programmes, in defining common research programmes and developing common computer tools and methodologies for safety assessment. SARNET comprises most of the actors\ninvolved in SA research in Europe, plus Canada. To reach these objectives, all the organizations networked in SARNET contribute to a Joint\nProgramme of Activities, which consists in:\n- Implementing an advanced communication tool for accessing all project information, fostering exchange of information, and managing documents;\n- Harmonizing and re-orienting the research programmes, and defining new ones;\n- Analyzing the experimental results provided by research programmes in order to elaborate a common understanding of relevant phenomena;\n- Developing the ASTEC code (integral computer code used to predict the NPP behaviour during a postulated SA), which capitalizes in terms of physical models the knowledge produced within SARNET;\n- Developing Scientific Databases, in which all the results of research programmes are stored in a common format (DATANET);\n- Developing a common methodology for Probabilistic Safety Assessment of NPPs;\n- Developing short courses and writing a text book on Severe Accidents for students and researchers;\n- Promoting personnel mobility amongst various European organizations.\nThis paper presents the major achievements after four and a half years of operation of the network, in terms of knowledge gained, of improvement of the ASTEC reference code, of dissemination of results and of integration of the research programmes conducted by the\nvarious partners.\nAfter this first period (2004-2008), co-funded by the EC, a further contract for the next four years is under negotiation with the EC as part of the 7th Framework Programme. During this period, the networking activities will focus mainly on the remaining pending issues as\ndetermined during the first period, experimental activities will be directly included in the common work and the network will evolve toward a complete self-sustainability. The bases for such an evolution are presented in the last part of the paper.
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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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.079 | 0.064 |
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".