Corrosion of Spent Nuclear Fuel and High-Level Waste Glass in Anoxic Clay Disposal Environment – Technical Note
Bibliographic record
Abstract
Many countries are pursuing deep geologic disposal of spent nuclear fuel (SNF) and/or high-level waste (HLW) glass in carbon steel containers in anoxic clay (and/or bentonite backfill) environments. If water contacts the nuclear material due to container failure by corrosion, corrosion (i.e., traditional dissolution) of SNF and/or HLW glass will occur potentially resulting in radionuclide release to the geosphere. Studies, generally independent of each other, have been conducted on corrosion of containers, corrosion of SNF and HLW glass, and radionuclide transport in the geological disposal setting. The output of these efforts is abstracted into integrated system performance assessments (PAs) in order to analyze the safety associated with generic deep geologic disposal systems of SNF and/or HLW glass (together also called HLW). This technical note considers the application of laboratory experimental test results from multiple research programs in the United States, Canada, Japan, and the European community on corrosion of SNF and/or HLW glass and mass transport in the clay/bentonite medium of a repository system. These studies also include work by staff at the U.S. Nuclear Regulatory Commission (NRC) and the Center for Nuclear Waste Regulatory Analyses (CNWRA). These are available to the public separately. Important examples include Ahn and Gwo (2020); and Pan and Ahn (2020, and 2018). In this additional paper-specific consideration is given to the corrosion of SNF and HLW glass controlled by aqueous transport of dissolved species in the aqueous media as the media is reaching solubility (or steady state) limits of the dissolved species.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".