The qualitative f‐ratio method applied to electron channelling‐induced x‐ray imaging with an annular silicon drift detector in a scanning electron microscope in the transmission mode
Bibliographic record
Abstract
Electron channelling is known to affect the x-ray production when an accelerated electron beam is applied to a crystalline material and is highly dependent on the local crystal orientation. This effect, unless very long counting time are used, is barely noticeable on x-ray energy spectra recorded with conventional silicon drift detectors (SDD) located at a small elevation angle. However, the very high count rates provided by the new commercially available annular SDDs permit now to observe this effect routinely and may, in some circumstances, hide the true elemental x-ray variations due to the local true specimen composition. To circumvent this issue, the recently developed f-ratio method was applied to display qualitatively the true net intensity x-ray variations in a thin specimen of a Ti-6Al-4V alloy in a scanning electron microscope in transmission mode. The diffraction contrast observed in the x-ray images was successfully cancelled through the use of f-ratios and the true composition variations at the grain boundaries could be observed in relation to the dislocation alignment prior to the β-phase nucleation. The qualitative effectiveness in removing channelling effects demonstrated in this work makes the f-ratio, in its quantitative form, a possible alternative to the ZAF method in channelling conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".