End of aging as a probe of finite-size effects near the spin-glass transition temperature
Bibliographic record
Abstract
We have measured the growth of the spin glass correlation length through the aging effect. Measurements were made on bulk ${\mathrm{Cu}}_{0.95}{\mathrm{Mn}}_{0.05}$ and a ${\mathrm{Cu}}_{0.88}{\mathrm{Mn}}_{0.12}$ thin film multilayer with CuMn layer thicknesses of 4.5 nm separated by 60-nm Cu layers. As the glass temperature ${T}_{g}$ is approached ($0.9{T}_{g} 0.96{T}_{g}$, there is no waiting time effect on the magnetization decay. In the temperature region $0.96{T}_{g}\text{--}1.00{T}_{g}$, all decays collapse onto a single decay curve indicating an end of aging even for long waiting times (${t}_{w}=10\phantom{\rule{0.16em}{0ex}}000s$). For the thin film, all effects due to the waiting time disappear at around $0.89{T}_{f}$, where ${T}_{f}$ is the freezing temperature marking the onset of irreversibility. These results are interpreted in terms of the spin glass correlation length saturating at a constant value after reaching a characteristic length scale, either the size of the crystallites in the bulk, or the thickness of the 4.5-nm film.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".