Calibrations for measurement of manganese and zinc in nail clippings using portable XRF
Bibliographic record
Abstract
A calibration method was developed to assess elemental concentrations in nail clippings, using a portable X‐ray fluorescence (XRF) device. Specifically, manganese and zinc were investigated in this study. Two sets of phantom nail clipping samples were prepared, one set containing manganese and one set containing zinc. In both sets, elemental concentrations in the phantom clippings were varied from 10 to 50 µg/g, in increments of 10 µg/g. Additionally, for each concentration, five distinct masses of sample were prepared ranging from 20 to 100 mg, in increments of 20 mg. XRF spectrometry was performed with the various samples using a portable X‐ray tube and detector system. Kα characteristic X‐rays were detected for both manganese and zinc. Intensities of detection were plotted against added concentration, resulting in linear relationships for both manganese and zinc. The slopes of these calibration lines were then examined as a function of sample mass. An empirical function was fit to the slope–mass relationship and compared with those obtained previously from other elements. By using this XRF calibration approach, it is possible to estimate elemental concentrations in human nail clippings for a variety of elements of medical interest. Copyright © 2013 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".