An Example of an INPRO Assessment of an INS in the Area of Waste Management
Bibliographic record
Abstract
Following a resolution of the General Conference of the IAEA in the year 2000 the International Project on Innovative Nuclear Reactors and Fuel Cycles, referred to as INPRO, was initiated. INPRO has defined requirements organized in a hierarchy of Basic Principles, User Requirements and Criteria (consisting of an indicator and an acceptance limit) to be met by innovative nuclear reactor systems (INS) in six areas, namely: economics, safety, waste management, environment, proliferation resistance, and infrastructure. If an INS meets all requirements in all areas it represents a sustainable system for the supply of energy, capable of making a significant contribution to meeting the energy needs of the 21st century. Draft manuals have been developed, for each INPRO area, to provide guidance for performing an assessment of whether an INS meets the INPRO requirements in a given area. The manuals set out the information that needs to be assembled to perform an assessment and provide guidance on selecting the acceptance limits and, for a given INS, for determining the value of the indicators for comparison with the associated acceptance limits. Each manual also includes an example of a specific assessment to illustrate the guidance. This paper discusses the example presented in the manual for performing an INPRO assessment in the area of waste management. The example, chosen solely for the purpose of illustrating the INPRO methodology, describes an assessment of an INS based on the DUPIC fuel cycle. It is assumed that uranium is mined, milled, converted, enriched, and fabricated into LWR fuel in Canada. The LWR fuel is assumed to be leased to a utility in the USA. The spent LWR fuel is assumed to be returned to Canada where it is processed into CANDU DUPIC fuel, which is then burned in CANDU reactors. The assessment steps and the results are presented in detail in the paper. The example illustrates an assessment performed for an INS at an early stage of development.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".