A miniature X‐ray tube approach to measuring lead in bone using L‐XRF
Bibliographic record
Abstract
Abstract Using a miniature X‐ray tube and silicon PiN diode detector, an approach to measuring lead (Pb) in bone phantoms was tested. The X‐ray tube was used to excite L‐line X‐ray fluorescence (L‐XRF) of lead in bone phantoms. The bone phantoms were made from plaster of Paris and dosed with varying quantities of lead. Phantoms were made in two sets with different shapes to model different bone surfaces. One set of bone phantoms was circular in cross‐section (2.5‐cm diameter), the other square in cross‐section (2.2 cm × 2.2 cm). Using an irradiation time of 180 s (real time), five trials were run for each bone phantom. Analysis was performed for both Lα and Lβ lead X‐rays. Based on these calibration trials, (3σ 0 /slope) minimum detection limits of 7.4 ± 0.3 µg Pb g −1 (circular cross‐section) and 8.6 ± 0.6 µg Pb g −1 (square cross‐section) were determined for the bare bone phantoms. To simulate a more realistic in vivo scenario with soft tissue overlying bone, further trials were performed with a resin material placed between the experimental system and the bone phantom. For the square cross‐section bone phantoms, a layer of resin with a thickness of 1.2 mm was used, and a minimum detection limit of 17 ± 3 µg Pb g −1 determined. For the circular cross‐section phantoms, a layer of resin with an average thickness of 2.7 mm was used. From these, a more realistic minimum detection limit for in vivo applications (43 ± 7 µg Pb g −1 ) was determined. Copyright © 2011 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".