Materials Research in Support of SCWR Development: Current Areas, Gaps and Needs
Bibliographic record
Abstract
International efforts on materials selection and development related to supercritical water-cooled reactors (SCWRs) have produced a considerable amount of data in the open literature [1], The majority of these data are on aspects of materials properties such as corrosion, stress corrosion cracking, creep, irradiation damage as well as microstructural degradation under various exposure conditions. These prior efforts are helping guide the current selection of candidate alloys for further, longer-term evaluation. As continuing research on the SCWR advances, gaps and limitations in the published data are being identified. In terms of corrosion properties, these gaps can be seen in several areas, including: 1) the test environment, 2) the physical and chemical severity of the tests conducted as compared with likely reactor service/operating condition, and 3) test methods used. While some of these gaps can be filled readily by the current research projects, in particular those occurring in Generation IV International Forum (GIF) member countries, others require further advances in our understanding of material-environment interactions involving supercritical water. Gaps in advanced test facilities for future research are also becoming evident. Future needs for materials development and suggestions for expanded international collaborations to link with groups working on materials for advanced fossil-fired supercritical water power plants, as well as other GEN IV and fusion reactor designs are summarized.
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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 | 0.024 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".