A calibration method for proposed XRF measurements of arsenic and selenium in nail clippings
Bibliographic record
Abstract
A calibration method for proposed x-ray fluorescence (XRF) measurements of arsenic and selenium in nail clippings is demonstrated. Phantom nail clippings were produced from a whole nail phantom (0.7 mm thickness, 25 × 25 mm(2) area) and contained equal concentrations of arsenic and selenium ranging from 0 to 20 µg g(-1) in increments of 5 µg g(-1). The phantom nail clippings were then grouped in samples of five different masses: 20, 40, 60, 80 and 100 mg for each concentration. Experimental x-ray spectra were acquired for each of the sample masses using a portable x-ray tube and a detector unit. Calibration lines (XRF signal in a number of counts versus stoichiometric elemental concentration) were produced for each of the two elements. A semi-empirical relationship between the mass of the nail phantoms (m) and the slope of the calibration line (s) was determined separately for arsenic and selenium. Using this calibration method, one can estimate elemental concentrations and their uncertainties from the XRF spectra of human nail clippings.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".