Safeguards and nuclear- powered submarines a model for special procedures on the nuclear fuel cycle
Bibliographic record
Abstract
This article focuses on the safeguards provided by the International Atomic Energy Agency (IAEA) and how to apply them to nuclear fuel used by nuclear-powered submarines (SSN) developed by a Non-Nuclear-Weapon State (NNWS).Brazil is developing its own SSN, and Australia -supported by the AUKUS partnership -will also operate an SSN around 2030.Countries such as the Republic of Korea, Iran, and Canada have already shown current or past interest in SSN.In this context, it is worth thinking about models to conciliate the safeguards provided by the IAEA and the development and operation of an SSN by an NNWS.The article presents a model in three steps.Firstly, it focuses on the normative framework of the IAEA on this issue.Secondly, it addresses the methodology and structure of the model.The last section presents the model building for each phase of the nuclear fuel cycle.The research outcome was the development of a model, structured following the nuclear fuel cycle, that combines four variables -NNWS interests, proliferation risks, safeguards, and possible key points of application of safeguards.This methodological approach makes the model unique and points out a future pathway of negotiation between the IAEA and an NNWS with an SSN program.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".