International radioactive waste for long-time disposal
Bibliographic record
Abstract
This thesis aims to reflect upon a problem of creating an International repository for long-term disposal of high-level radioactive waste. At the moment, there is no solution to the problem of long-term disposal of spent nuclear fuel and highly radioactive waste, although volumes of the latter are rapidly growing. The author of the thesis uses her academic background to tackle the issue from political, socio-economic and technical points of view. Approximately 270 000 tons of spent nuclear fuel have been saved up and its number increases by 12 000 tons annually. The overload of on-site repositories requires prompt measures to replace storage with disposal. The option of managing those waste on the best possible terms, implying finding the safest, most technically advanced and legally proven solution was advised by IAEA in a context of building an International Deep geological repository for High-level waste (HLW) long-term storage. The findings indicate that many countries have already accepted the technology of Deep geological storage for HLW as the most advanced nowadays, from all of the existing HLW management strategies. However, not all of those countries own technical and economic capacities for implementing domestic programmes for HLW Deep geological storage. Moreover, the insecure level of communication with public within some countries, brings mistrust towards the scientifically proven projects and triggers protest and unacceptance, that postpones an acute problem solution for later generations. In this context, the options of reaching international cooperation via building the Deep geological repository for SNF and HLW under the aegis of the IAEA, could bring Member States a big step forward towards a long-term nuclear waste disposal.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".