Bibliographic record
Abstract
The development of nuclear waste management policy continues across the world. The leading countries in this sector remain in Europe where the market for decommissioning is estimated at approximately €4.5 billion in 2016 and is expected to double by 2021. There were a few significant changes in 2016, one of which was the administrative action in the European Union (EU). As a follow-up to the 2015 requirement for member states to notify the European Commission of their national nuclear waste management programs by August 2015, the Commission found that, for example, Latvia did not fully transpose into national law EU Directive 2011/70 Establishing a Community Framework for the Responsible and Safe Management of Spent Fuel and Radioactive Waste (Radioactive Waste Directive). This builds on the 2014 reports: (1) Radioactive Waste Management Stakeholders Map in the European Union: Report May 2014 and (2) Management of Spent Nuclear Fuel and Its Waste (produced by both the European Commission and the European Academies’ Science Advisory Council). Both of these reports are designed to assist EU member states in meeting their requirements under the Radioactive Waste Directive, which requires them to establish a dedicated policy, including the implementation of national programs for the management of spent fuel and radioactive waste. Internationally, however, other global leaders in this area—for example, Canada and the United States—continue to make little progress.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".