Comparing Legacy Waste Management to Advanced Reactor Waste Management
Bibliographic record
Abstract
The Nuclear Energy Agency (NEA) and Natural Resources Canada (NRCan) are organizing an international workshop on the implementation of radioactive waste management and decommissioning strategies in small modular reactors (SMRs) and advance reactor technologies. The event will take place in Ottawa, Canada on 7-10 November 2022. The workshop will convene participants from various fields of expertise in the areas of radioactive waste management, decommissioning, nuclear science and development, transportation, as well as young professionals, communication experts and researchers. The goal of the workshop is to devise a guideline document that will serve implementers in understanding key issues in decommissioning and waste management of new reactors from the design perspective, aiding in the licensing process and in future decommissioning and waste management activities. DOE has invested considerably in the innovation of advanced reactors. Interaction in this workshop allows INL and DOE to articulate the importance of looking at the back-end of the fuel cycle for advanced reactors. The back-end of the fuel cycle is important to the success of advanced reactors, and DOE may need to manage this material in the future after it is discharged from reactors. I have been asked to present at the track titled "Operational and Design Optimization Consideration Related to Decommissioning and Radioactive Waste Management for SMRs/Advanced Reactors".
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".