European Lead Fast Reactor (ELSY and LEADER projects)
Bibliographic record
Abstract
It is already 10 years since the European High Temperature Reactor (HTR) Technology Network (HTR-TN) launched a programme for development of HTR Technology, which expanded so far through 3 successive Euratom Framework Programmes. Many projects have\nbeen performed in line with the Network strategy that defined early, beyond mere electricity generation, near-term co-generation of heat and power (CHP) as the main specific contribution of HTR to the growing needs of European industry for this type of energy supply. HTR can fulfil such a versatile mission due to the unique potential brought by its high operating temperature and by its unique flexibility obtained from its large thermal inertia and its low power compared to other nuclear systems. HTR-TN strategy has been recently integrated in the global strategy proposed by SNETP in its "Strategic Research Agenda" as one of the 3 "pillars" of future European nuclear\nR&D. It is also in line with the orientations and the timing of the "Strategic Energy Technology Plan (SET-Plan)" for the development of CO2-lean energy technologies and thus strengthens the nuclear option in a future European energy mix. The recently created SNETP\nworking group on nuclear cogeneration with strong end-user participation will work out a Nuclear Cogeneration Industrial Initiative (NC2I) aiming at demonstration of this technology consistently with the SET-Plan agenda for curbing CO2 emissions.
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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 | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.085 | 0.039 |
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".