Optimization of L‐shell X‐ray fluorescence detection of lead in bone phantoms using synchrotron radiation
Bibliographic record
Abstract
Closely related toxicity and retention mechanisms of lead (Pb) in the human body involve the bone tissues where Pb can accumulate and reside on a time scale ranging from years to tens of years. In vivo measurements of bone Pb can, therefore, play an important role in a comprehensive health risk assessment of Pb exposure. In vivo L‐shell X‐ray fluorescence (LXRF) measurement of bone Pb was first demonstrated over 4 decades ago. Implementation of the method, however, encountered challenges associated with low sensitivity and calibration procedure. In this study, the LXRF measurement was optimized by varying the incident photon energy and the excitation‐detection geometry. The Canadian Light Source synchrotron radiation was used to compare 2 different excitation‐detection geometries of 90 ° and 135 ° using 3 different X‐ray photon energies: 15.8, 16.6, and 17.5 keV. These energies optimized excitation of the L 3 subshell of Pb and simulated the most intense K‐shell emissions of zirconium, niobium, and molybdenum, respectively. Five rectangular plaster‐of‐Paris bone phantoms with Pb concentrations of 0, 7, 17, 26, and 34 μg/g, and one rectangular 3.1‐mm‐thick resin phantom mimicked the X‐ray attenuation properties of human bone and soft tissue, respectively. Optimal LXRF detection was obtained by the 15.8‐keV energy and the 90 ° and 135 ° geometries for the bare bone and the bone and soft tissue phantoms, respectively.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".