A New Method for Dead Time Calibration and a New Expression for Correction of WDS Intensities for Microanalysis
Bibliographic record
Abstract
Observed photon count rates must be corrected for detector dead time effects for accurate quantification, especially at high count rates. We present the "constant k-ratio" method, a new approach for calibrating dead time for wavelength dispersive spectrometers by measuring k-ratios as a function of beam current. The method is based on the observation that for a given emission line at a specific take-off angle and electron beam energy, the intensity ratio from two materials containing the element should remain constant as a function of beam current, if the dead time calibration is accurate. The method has the advantage that it does not rely on the linearity of the beam current picoammeter, yet also allows the analyst to evaluate the picoammeter linearity, another critical parameter in EPMA calibration. By simultaneously comparing k-ratios for all spectrometers, one can also ascertain k-ratio consensus, essential for inter-laboratory comparisons. We also introduce improved dead time expressions and provide best practices on how to perform these instrument calibrations using this new "constant k-ratio" method. These improvements enable quantitative analysis of major and minor elements with high accuracy at high beam currents, simultaneously with trace elements with high sensitivity, for point analyses and X-ray mapping.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".