Strontium depth distribution in human bone measured by micro‐PIXE
Bibliographic record
Abstract
Abstract Strontium is naturally present in human bone, where it may exert beneficial or detrimental health effects depending on its concentration. The way strontium influences bone health and the concentrations at which the beneficial/detrimental health effects of strontium become important are still unknown, due partly to the difficulty of assessing the bone strontium concentration in vivo non‐invasively. An x‐ray fluorescence (XRF) system was developed, which is capable of measuring normal bone strontium levels in vivo non‐invasively and therefore has the potential of becoming an important tool to understand bone strontium health effects. This technique, however, has the limitation that it relies on assumptions about bone strontium distribution to obtain a quantitative strontium measurement. To understand, to which degree, if any, the bone strontium distribution in bone changes from person to person, the bone strontium depth distribution was explored using micro‐PIXE in five ex vivo cadaver fingers, taken from normal people exposed to strontium only through diet. We found that, in the measured samples, strontium was uniformly distributed across the cortical and trabecular bone, and therefore corrections do not need to be applied to in vivo strontium XRF data to correct for strontium depth distribution. Copyright © 2009 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".