Assessment of RepU Recycling Contribution to ESG Performance in French Nuclear Power Plant with a LCA “Pilot Study”
Bibliographic record
Abstract
Although nuclear energy is acknowledged for its low CO2 emission, the fuel cycle front-end footprint can be further decreased through the use of recycled nuclear material resulting from spent fuel reprocessing.This paper presents a typical case of footprint reduction obtained with the use of Reprocessed Uranium (RepU) in Cruas French Nuclear Power Plant.The demonstration is based on a Life Cycle Assessment (LCA) screening study, also called "pilot study" in the paper, realized following LCA general method with Simapro © LCA software and ecoinvent 3.6 Database.Its main result is a significant decrease of climate change indicator (around 40%) compared to the 2022 EDF nuclear kWh LCA study.The calculation was extended by integrating two other LCA indicators, "particle matter/respiratory inorganics" and "resource depletion", for exploratory purposes.This pilot study will be improved as new data become available, with the purpose to lead to a standardized LCA study.It will also be a tool to identify and to quantify ways to further reduce RepU fuel cycle environmental footprint.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".