Final Report: Characterization of Canister Mockup Weld Residual Stresses
Bibliographic record
Abstract
Stress corrosion cracking (SCC) of interim storage containers has been indicated as a high priority data gap by the Department of Energy (DOE) (Hanson et al., 2012), the Electric Power Research Institute (EPRI, 2011), the Nuclear Waste Technical Review Board (NWTRB, 2010a), and the Nuclear Regulatory Commission (NRC, 2012a, 2012b). Uncertainties exist in terms of the environmental conditions that prevail on the surface of the storage containers, the stress state within the container walls associated both with weldments as well as within the base metal itself, and the electrochemical properties of the storage containers themselves. The goal of the work described in this document is to determine the stress states that exists at various locations within a typical storage canister by evaluating the properties of a full-diameter cylindrical mockup of an interim storage canister. This mockup has been produced using the same manufacturing procedures as the majority of the fielded spent nuclear fuel interim storage canisters. This document describes the design and procurement of the mockup and the characterization of the stress state associated with various portions of the container. It also describes the cutting of the mockup into sections for further analyses, and a discussion of the potential impact of the results from the stress characterization effort.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 0.001 |
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".