Contrast factors of irradiation-induced dislocation loops in hexagonal materials
Bibliographic record
Abstract
Irradiation-induced defects, such as dislocation loops, significantly affect the mechanical properties of structural alloys, altering slip and influencing creep and growth. As a consequence, the quantitative characterization of irradiation-influenced defect structures as a function of dose, thermal treatments and/or cold work is essential for models which predict changes in mechanical properties due to the accumulation of irradiation defects. Whole pattern diffraction line profile analysis (DLPA) is a modern tool for microstructure characterization based on first-principles physical models, well established for dislocation density measurements in plastically deformed materials. However, the DLPA procedures that have been tailored for deformed materials account for the strain anisotropy of hexagonal crystals with theoretical contrast factors calculated specifically for dislocation types generated by plasticity which, if directly applied to irradiated materials, will inherently introduce inaccuracies. In an effort to specifically address dislocation structures consisting of irradiation defects, a method was developed to calculate theoretical contrast factors for any general elliptically shaped dislocation loop. The values of the contrast factors are calculated and compiled in tables for six common elliptical 〈a〉-type and 〈c + a〉-type loops for ten hexagonal crystals, in order to provide a database for future DLPA work on irradiated materials. The use of the dislocation loop specific contrast factors is demonstrated on neutron-irradiated Zr–2.5Nb.
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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 | 0.001 | 0.001 |
| 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".