XRD<sup>2</sup> Stress Measurement for Samples with Texture and Large Grains
Bibliographic record
Abstract
Stress measurement on samples with texture and large grains is always a challenge. The diffraction peak intensity varies dramatically with different sample orientation. The macroscopic elasticity becomes anisotropic due to strong preferred orientation. The large grains may results in a big error in 2θ due to poor sampling statistics. The fitting results of the conventional sin2ψ method is extremely sensitive to texture and large grains. When stress is measured with a 2D detector, most of the above adverse effects can be minimized or eliminated. The data integration helps to smooth out rough diffraction profiles due to large grain size, texture, small sample area or weak diffraction. The large angular coverage and multiple diffraction rings can minimize the effect of the macroscopic anisotropy. The weighted least squares regression and intensity threshold can further reduce the effect of poor statistics associated with texture and large grains. Multiple {hkl} rings may be used to measure the stress to improve the statistics and minimize the elastic anisotropy effect.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.072 | 0.019 |
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".