Investigating Methods of Normalization for X-ray Fluorescence Measurements of Zinc in Nail Clippings Using the Topas Monte Carlo Code
Bibliographic record
Abstract
Development of portable X-ray fluorescence devices has made it easier to quickly assess trace elements such as zinc in human tissue. Zinc deficiency can have serious implications for growth and development of the human body. From recent studies, zinc content in nail clippings has been suggested to be an effective biomarker for zinc status. In this study, a Monte Carlo simulation approach was used to investigate the use of a portable X-ray fluorescence system for detecting zinc in nail clippings. The portable X-ray device was modelled using specifications from the manufacturer input to the TOPAS Monte Carlo code (Geant4 simulation software). The simulations were carried out for varying nail clipping thickness (0.3 mm – 1.2 mm) as clipping thickness has previously been shown to influence zinc signal in experimental trials. Each simulation was run for 10 histories and the statistical uncertainty was less than 3.5%. The obtained energy spectra from different measurements were analyzed and three different normalization techniques (coherent, Compton, and entire spectrum) were introduced. General agreement between simulation and experimental trends were observed, indicating successful benchmarking of the simulation design. All three normalization techniques showed a slightly negative trend for signal with respect to clipping thickness, consistent with experimental results. The simulation results suggested that coherent normalization can be a particularly robust normalization procedure, showing a small relative variation in signal over a wide range of clipping thickness.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".