Is a sufficient measure of the standard uncertainty in X‐ray spectroscopy?
Bibliographic record
Abstract
There is a magnitude larger scatter in the experimental data of fundamental parameters than the claimed error estimate. We give examples from recent compilations of excitation and decay parameter values for the untenable large scatters, indicating methodological problems. One is the improper use of uncertainty estimation. The measured spectrum is not expected to follow Poisson distribution. We report proper statistical uncertainty calculations. It implies a two to five times larger uncertainty but still does not account for the large scatter. The other possible explanation could be rooted in the ill‐posed problem of exponential analysis, as radiation measurement belongs to this category. We give evidence from particle‐induced X‐ray emission and X‐ray fluorescence for additional exponential terms, thus leading to multi‐exponential analysis. This could explain the large scatter, as the usual square root of counts rule cannot be used for the standard uncertainty. We present a novel approach where discriminators are used to reduce the number of exponentials and the discriminated events are also processed and collected into a separate spectrum. Analyzing both spectra and the live time and dead time clocks allows the determination of the true input counts. It is a non‐extended dead time approach. With this approach, we have a much reduced statistical uncertainty, and both the total spectrum and the fractional spectrum have the same uncertainty. As an independent quality assurance tool, the time interval histogram analysis is also presented. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.063 | 0.206 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".