Ensuring Future Skills: Education and Training in Underground Waste Disposal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Progress has been slow in solving the radioactive waste disposal problem, with wide swings in the resources deployed in national programs over the last 30years. Disposal programs takes decades to implement. There is already a problem of maintaining the expertise base and ensuring that trained scientists, engineers, and policy makers will be available when and where they are needed. Internationally, we need to ensure that this problem does not undermine the capability to provide safe and secure waste management solutions. New initiatives are establishing organizations to help resolve this problem. In this paper we look at the nature of the education and training needs, what resources are available, and how knowledge and experience might be propagated into the future. The future outlook is mixed. Despite the need, the funding of training is still widely regarded as of low priority and seems often to be regarded as a marginal organizational expenditure. Provision of high quality education requires much preparation, access to large facilities, and the input of the best expertise.
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.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it