Point defect generation, nano-void formation and growth. I. Validation
Bibliographic record
Abstract
The volume fraction of point defects generated as a function of plastic shear strain squared, γ2, was derived from crystal plasticity concepts. The evolution was determined from the stress–strain values using a new constitutive relation which replicates the measured behavior with at least two fitted loci. Assuming that nano-voids form by clustering of vacancies, the nano-void diameter was found to be proportional to their spacing and shear strain with the constant being characteristic of point defect production during deformation. The predicted amount of point defect generated was validated using the previously determined resistivity of [100] copper single crystals deformed at 4.2 K and annealed at 296 K. Similar analysis of super-pure polycrystalline copper data affirmed that the dynamic annihilation parameter extrinsically incorporated in the new derivation is larger due to formation of slip clusters. Moreover, the temperature dependence of the mean slip-distance to inter-forest spacing ratio at Stage II to III transition indicates that the thermally activated drag of vacancy-creating jogs occurs above 150 K. For polycrystalline aluminum deformed at 296 K, it was concluded that the nuclei of the nano-voids were not part of the evolving dislocation array but were embedded in the grown-in microstructure. This hypothesis is pursued in the accompanying paper, Part II, and its prediction results in a criterion for ductile failure.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".