User Requirements and Criteria for Proliferation Resistance in INPRO
Bibliographic record
Abstract
In designing future nuclear energy systems, it is important to consider the potential that such systems could be misused for the purpose of producing nuclear weapons. INPRO set out to provide guidance on incorporating proliferation resistance into innovative nuclear energy systems (INS). Generally two types of proliferation resistance measures are distinguished: intrinsic and extrinsic. Intrinsic features consist of technical design features that reduce the attractiveness of nuclear material for nuclear weapon program, or prevent the diversion of nuclear material or production of undeclared nuclear material for nuclear weapons. Extrinsic measures include commitments, obligations and policies of states such as the Treaty on the Non-Proliferation of Nuclear Weapons (NPT) and IAEA safeguards agreements. INPRO has produced five basic principles and five user requirements for INS. It emphasizes that INS must continue to be an unattractive means to acquire fissile material for a nuclear weapon program. It also addresses as user requirements: 1) a balanced and optimised combination of intrinsic features and extrinsic measures, 2) the development and implementation of intrinsic features, 3) an early consideration of proliferation resistance in the development of INS and 4) the utilization of intrinsic features to increase the efficiency of extrinsic measures. INPRO has also developed criteria, consisting of indicators and acceptance limits, which would be used by a state to assess how an INS satisfies those user requirements. For the first user requirement, the most important but complex one, INPRO provides a 3-layer hierarchy of indicators to assess how unattractive a specific INS would be as part of a nuclear weapon program. Attributes of nuclear material and facilities are used as indicators to assess intrinsic features. Extrinsic measures imposed on the system are also assessed. Indicators to assess defence in depth for proliferation resistance include the number and robustness of barriers, and the redundancy or complementarity of barriers. The cost of incorporating proliferation resistant features is used to assess the cost-effectiveness of any particular INS in providing proliferation resistance. The stages in the development of an INS at which proliferation resistance is considered in the process are assessed. Awareness of extrinsic measures by designers and use of intrinsic features for verification illustrate how intrinsic features facilitate extrinsic measures. An INPRO-consistent methodology to assess the proliferation resistance of an INS is still under development, with feedback expected from the case studies undertaken by Argentina, India, Russia and the Republic of Korea.
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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 | 0.052 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".