Primary damage production in the presence of extended defects and growth of vacancy-type dislocation loops in hcp zirconium
Bibliographic record
Abstract
Production rates in long-term predictive radiation damage accumulation models are generally considered independent of the material's microstructure for reactor components. In this study, the effect of preexisting microstructural elements on primary damage production in \ensuremath{\alpha}-Zr---and vice versa---is assessed by molecular dynamics (MD) simulations. $a$-type dislocation loops, $c$-component dislocation loops, and a tilt grain boundary (GB) were considered. Primary damage production is reduced in the presence of all these microstructural elements, and clustering behavior is dependent on the microstructure. Collision cascades do not cause $a$-type loop growth or shrinkage, but they cause $c$-component loop shrinkage. Cascades in the presence of the GBs produce more vacancies than interstitials. This result, as well as other theoretical, MD, and experimental evidence, confirms that vacancy loops will grow in the vacancy-supersaturated environment near GBs. Distinct temperature-dependent growth regimes are identified. Also, MD reveals cascade-induced events where $a$-type vacancy loops are absorbed by GBs. Fe segregation at the loops inhibits this cascade-induced absorption.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".