Bibliographic record
Abstract
Despite decades of effort, the nuclear industry does not yet have a working solution for managing spent fuel and high level waste, the most radioactive products generated by nuclear power plants. Although many scientific and technical bodies have endorsed geological disposal as the preferred solution to this problem, there remain significant uncertainties about the long‐term performance of repositories and behavior of the nuclear wastes to be stored in these facilities. Apart from a minority of countries, most countries have not chosen any sites for a repository. Further concerns about the long‐term safety of repositories arise from the experiences of failures and accidents at pilot facilities. One reason for the absence of operating repositories decades after they were first proposed is widespread public opposition to such facilities. Polls have revealed that substantial majorities of people consider nuclear waste with dread and do not approve plans to dispose of radioactive wastes near them, or, often, far away either. Nuclear power advocates have typically dismissed public concerns as resulting from a lack of understanding of scientific facts but this explanation does not withstand scrutiny. Technical approaches to dealing with nuclear waste, such as reprocessing of spent fuel, mischaracterize the social concerns and therefore do not help gain public acceptance. Concern about radioactive waste has contributed to the failure of the propaganda effort by the nuclear industry to market nuclear power as a solution to climate change. The absence of a solution to waste negatively affects the future expansion of nuclear energy. This article is categorized under: Nuclear Power > Climate and Environment Nuclear Power > Economics and Policy Nuclear Power > Science and Materials
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".