Research and Development on Accelerator-Driven Systems
Bibliographic record
Abstract
A fast subcritical reactor coupled to a particle accelerator is a concept of transmutation system that would allow large quantities of minor actinides to be burned efficiently. The research on transmutation encompassing accelerator-driven systems (ADS) is a part of the EURATOM research and development programme that lies within the area of partitioning and transmutation (P&T) of long-lived radionuclides in nuclear waste. P&T is one of the most notable research areas of the EURATOM Fifth (1998-2002) as well as the Sixth (2002-2006) Framework Programmes (FP). The objective of the research work in this area is to determine practical ways of reducing the amount and/or hazard of the waste to be disposed of. In FP5, there are 13 projects in this area with a total budget of about 69 M€ EU contribution is about 28 M€ In FP6, the research in this area, with a EU contribution of about 30 M€, strengthens the work that has been carried out in FP5 with a view to building a European Research Area (ERA) in this field. This has lead to integrating all EU activities on partitioning into one integrated project, and the same is planned for the transmutation activities. Moreover, a targeted project concerning the impact of P&T on waste management has also been initiated. International co-operation in the area of P&T with non-EU countries (such as Canada, USA and Japan), including the Commonwealth of Independent States (CIS) is also outlined.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".