<i>ZAFG</i> Method for Quantitative Characterization of Spherical Particles: Deriving a Universal Equation for Geometrical Correction
Bibliographic record
Abstract
This study introduces a universal equation to calculate the geometrical correction factor (G) as the fourth factor in the conventional ZAF method for quantifying spherical particles (specifically, NIST-K411 glass microspheres mounted on bulk carbon substrate). Note that the fluorescence correction factor (F) is not considered in this study. Our findings demonstrate that the G factor, as a function of the particle diameter (D) and the range of emitted X-rays in a bulk sample (Xe), provides the best model. Xe depends on the chemical composition and accelerating voltage. We observed excellent agreement between the G factor predicted by our model and experimental data obtained from NIST-K411 standard particles. Our results show that when Xe is greater than D, the G factor decays exponentially, independent of the incident electron energy, X-ray lines, and chemical composition of the particles. We also found that when DXe > 1, the particle behaves as a bulk sample, and G = 1. Notably, our data indicate that the G factor depends only on DXe, not on the chemical composition or beam energy.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".