Modeling of the Simultaneous Evolution of Vacancy and Interstitial Dislocation Loops in hcp Metals Under Irradiation
Bibliographic record
Abstract
Abstract We present a model of the homogeneous nucleation and growth of vacancy and interstitial loops in irradiated hcp metals, which allows one to find the size distribution function, the dose dependence of the mean parameters of the dislocation system, and to describe effects due to temperature, material parameters, and initial microstructure. The model is based on a hierarchy of coupled ordinary differential equations. The first two equations are the rate equations for vacancy and interstitial concentrations. Other equations describe random walks of interstitial and vacancy clusters in a size space, i.e., the time dependence of loop densities. As an input, the model contains the capture efficiencies of point defects by loops, which depend self-consistently on the loop size and dislocation density. We have considered two possible scenarios depending on the point defect dilatation volume ratio: (i) dislocation bias for interstitial atoms and (ii) dislocation bias for vacancies. The model results are qualitatively consistent with experimental observations of a coexistence of interstitial and vacancy dislocation loops on the same habit planes in Zr and other hcp metals. The temperature dependence of the resulting loop size distributions depends strongly on the material properties and the initial microstructure.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".