Lessons from national approaches: a long uphill struggle in search of sites for repositories for nuclear waste locations
Bibliographic record
Abstract
A quarter of a century ago, a long-term expert in the nuclear waste scene stated that “the management of spent nuclear fuel and high-level nuclear waste has the deserved reputation as one of the most intractable policy issues facing the United States and other nations using nuclear reactors for electric power generation” (North, 1999, p. 751). Apart of exceptions, this statement is still true. At some points, however, there is light at the end of the tunnel if we can read the signs of the times. It will be a long hike, in steep terrain, poor visibility and with an approximate destination. We need a safe and acceptable site, tolerated by the affected parties, where a repository can be built, operated and, finally, closed down in reasonable course by a generation to come and with a clear conscience. This contribution does not present the silver bullet (which does not exist) but suggests some criteria and characteristics which have not been respected in the history of final disposal – but they should be. It needs adequate resources: stable structures, competent institutions, learning personnel (in institutions and civil society), mature and open discourse as well as sufficient time. Based on https://doi.org/10.5194/egusphere-egu24-6514 (Flüeler, 2024a).
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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 | 0.024 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.030 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".