European Lead Fast Reactor (ELSY and LEADER projects)
Bibliographic record
Abstract
In order to optimise the use of the available means and to constitute sustainable research groups in the European Union, the Severe Accident Research NETwork of Excellence (SARNET) has gathered 51 organisations representing most of the actors involved in Severe Accident (SA) research in Europe plus Canada. This project was co-funded by the European Commission (EC) under the 6th Framework Programme. Its objective was to resolve the most important pending issues for enhancing, in regard of SA, the safety of existing and future Nuclear Power Plants (NPPs). \nSARNET tackled the fragmentation that existed between the national R&D programmes, in defining common research programmes and developing common computer codes and methodologies for safety assessment. The Joint Programme of Activities consisted in:\n-\tImplementing an advanced communication tool for accessing all project information, fostering exchange of information, and managing documents;\n-\tHarmonizing and re-orienting the research programmes, and defining new ones;\n-\tAnalyzing the experimental results provided by research programmes in order to elaborate a common understanding of relevant phenomena;\n-\tDeveloping the ASTEC code (integral computer code used to predict the NPP behaviour during a postulated SA) by capitalizing in terms of physical models the knowledge produced within SARNET;\n-\tDeveloping Scientific Databases, in which the results of research experimental programmes are stored in a common format;\n-\tDeveloping a common methodology for Probabilistic Safety Assessment of NPPs;\n-\tDeveloping short courses and writing a text book on Severe Accidents for students and researchers;\n-\tPromoting 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 improvements of the ASTEC reference code, of dissemination of results and of integration of the research programmes conducted by the various partners.\nMost initial objectives were reached but the SARNET follow-up network, co-funded by EC in the 7th Framework Programme (SARNET2 project), will consolidate the first assets and focus mainly on the highest priority pending issues as determined during the first period. The objective will be also to make the network evolve toward a complete self-sustainability.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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.
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 teacher head, 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".