Monte Carlo investigations of distance-dependent effects on energy deposition in K-shell x-ray fluorescence bone lead measurement
Bibliographic record
Abstract
Radiation energy deposition results are presented from a Monte Carlo code simulating the lower part of a leg during an in vivo 109Cd K-shell x-ray fluorescence (KXRF) bone lead measurement. The simulations were run for a leg phantom model representing an adult subject, assuming concentrations of 10 microg Pb per gram bone mineral and tracing 500 million photons in each simulation. Trials were performed over a range (0.5-6.0 cm) of source-to-sample (S-S) distances. Energies deposited due to Compton and photoelectric processes occurring in the bone and the soft tissue were obtained. The data show an increase in the amount of energy deposited in the bone as the sample is moved closer to the source (from 2.0 cm to 0.5 cm). However, there is a decrease in the amount of energy deposited in the soft tissue as the sample is moved closer to the source over the same distance interval. In decreasing the S-S distance from 2.0 cm to 0.5 cm, the amount of energy deposited in the sample as a whole was found to increase by 11%. By calculating the energy deposition in the bone and in the soft tissue as a fraction of the total energy deposited in the sample, the corresponding changes are quantified as a function of S-S distance. Similarly, the proportions of energy deposited via the photoelectric effect and Compton scattering are presented as a function of S-S distance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".