A Comparison of a Direct Electron Detector and a High-Speed Video Camera for a Scanning Precession Electron Diffraction Phase and Orientation Mapping
Bibliographic record
Abstract
A scanning precession electron diffraction system has been integrated with a direct electron detector to allow the collection of improved quality diffraction patterns. This has been used on a two-phase α–β titanium alloy (Timetal® 575) for phase and orientation mapping using an existing pattern-matching algorithm and has been compared to the commonly used detector system, which consisted of a high-speed video-camera imaging the small phosphor focusing screen. Noise is appreciably lower with the direct electron detector, and this is especially noticeable further from the diffraction pattern center where the real electron scattering is reduced and both diffraction spots and inelastic scattering between spots are weaker. The results for orientation mapping are a significant improvement in phase and orientation indexing reliability, especially of fine nanoscale laths of α-Ti, where the weak diffracted signal is rather lost in the noise for the optically coupled camera. This was done at a dose of ~19 e−/Å2, and there is clearly a prospect for reducing the current further while still producing indexable patterns. This opens the way for precession diffraction phase and orientation mapping of radiation-sensitive crystalline materials.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".